//BEGIN:VCALENDAR VERSION:2.0 PRODID:-//GRAPES - ECPv6.16.3//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:GRAPES X-ORIGINAL-URL:http://grapes-network.eu X-WR-CALDESC:Events for GRAPES REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD TZOFFSETFROM:+0000 TZOFFSETTO:+0000 TZNAME:UTC DTSTART:20200101T000000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20190331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20191027T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20200329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20201025T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20210328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20211031T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20240403 DTEND;VALUE=DATE:20240406 DTSTAMP:20260613T034856 CREATED:20240122T181609Z LAST-MODIFIED:20240406T205305Z UID:2220-1712102400-1712361599@grapes-network.eu SUMMARY:Final open conference and career fair DESCRIPTION:[slideshow_deploy id=’2229′] \nGRAPES’ Final event will take place in Linz during the first week of April 2024 from Wednesday\, April 3 to Friday\, April 5. It is organised by JKU & RISC and it will be co-located with the Special Semester on Rigidity and Flexibility which will take place at RICAM from February 26-May 17\, 2024. RICAM provides some accomodation and travel support for researchers attending the workshop “Rigidity in Action”\, April 8-12 – if you are staying for one of the workshops\, please register at https://www.ricam.oeaw.ac.at/specsem/specsem2024/registration/. \nDuring the GRAPES Final conference ESRs shall present their work and interact with other GRAPES and external industrial groups to discuss career opportunities. There will also be presentations by project partners: Denys Plakhotnik – ModuleWorks\, Christoph Hofer – RISC-Software\, ITI\, members: Vasileia Filidou – Athena RC\, and invited guest: J. Brandstetter – Institue for Machine Learning\, NXAI\, as part of the Career Fair. Lastly\, the event will host a couple of invited talks related to the special semester. \n[slideshow_deploy id=’2385′] \nRegistration\nRegistration is now closed.\n \nLocation\nThe meeting will take place at the Johann Radon Institute (RICAM)\, Science Park 2\, 4th floor (map) \nOn Thursday lunch will be served at the Mensa (5 minutes from the venue\, at campus). Coupons will be distributed during the breaks in the morning session. \nThe workshop’s dinner on Thursday 4/4 will take place at the restaurant https://www.schlossbrasserie.at/\, 5 minutes from the tram station Hauptplatz (steep upwards). Dinner starts at 8pm.\n \nSchedule \nLat update: 2-4-2024\n\n\n\n\n Wednesday\, April 3rd\n\n\n 14:15-15:00ESR presentations: Carles Checa -- SLIDES\, Thanasis Zounpekas\n\n\n 15:00-15:30Coffee break\n\n\n 15:30-16:15Christopher Brampton (ITI) \n\n\n 16:15-17:00Johannes Brandstetter (Institue for Machine Learning\, NXAI)\n\n\n 17:15Supervisory Board meeting -\n\n\n 18:30Welcome reception\n\n\n \n\n\n \n\n\n Thursday\, April 4th\n\n\n 9:30-10:30ESR presentations: Rao Fu \, Shahroz Khan\, E. Hoxhaj\n\n\n 10:30-11:00Coffee break\n\n\n 11:00-12:00Vasileia Filidou - Τips for successful proposal applications \n\n\n 12:00-14:00Lunch break\n\n\n 14:00-14:45Audie Warren - Components of Configurations\n\n\n 14:45-16:00ESR presentations: Jean Michel Menjanahary -- SLIDES\, Mahenina Ramanantoanina\, Michelangelo Marsala \n\n\n 16:00-16:30Coffee break\n\n\n 16:30-17:15Denys Plakhotnik (ModuleWorks) \n\n\n 20:00Dinner\n\n\n \n\n\n Friday\, April 5th\n\n\n 9:30-10:15Johannes Siegele - Motion Polynomials -- SLIDES \n\n\n 10:15-10:35Coffee break\n\n\n 10:35-11:15ESR presentations: Arturs Berzins\, Pablo Gonzalez-Mazon -- SLIDES \n\n\n 11:15-12:00Christoph Hofer (RISC-Software)\n\n\n \n\n\n\n \nTravel directions\nBy car\nThe campus is right next to the “Dornach” exit of the A7 – Mühlkreisautobahn\, with Passau\, Salzburg and Vienna all being roughly a 1:30 hour’s drive away. \nBy train\nLinz is a railway hub with Intercity lines connecting it to Vienna\, Salzburg/Munich\, Passau/Nürnberg and Prague. \n — ÖBB (Austrian Federal Railways)\n — Westbahn: Train from Vienna West Station (Westbahnhof) to Linz and from Salzburg to Linz \nFrom the railway station\, you can use: \n • a taxi (approx. 15 minutes\, about 20 Euros)\n • public transport (download the tram map):\n — purchase with cash a MIDI (1 hour) or MAXI (1 day) ticket from a ticket machine or from a tobacco store (“Trafik”)\n — take tram number 1 or 2 (direction “Universität”);\n — alight at the final stop “Universität” (approx. 30 minutes)\n — from there\, walk along the Altenberger Straße to find the Science Park on the right hand side. \nBy plane \n• Vienna International Airport (airport code VIE): \n — Connection Vienna airport to Linz train station: ÖBB (Austrian Federal Railways) offers direct IC/Railjet connections (every 30 minutes) between Vienna airport and Linz train station via Vienna Central Station (Wien Hauptbahnhof). \n — Connection Vienna airport to Vienna West Station (Westbahnhof): There is a shuttle bus (Vienna AirportLines) every 30 minutes directly to the Vienna West Station (Westbahnhof).\n \n• Blue Danube Airport Linz (airport code LNZ):   Public transport\, Airport shuttle\, Taxi\, rental car.\n \nAccommodation suggestions\nhttp://www.sommerhaus-hotel.at/de/linz close to campus\nhttps://harrys-home.com/linz-urfahr/ close to campus\nhttps://www.hotelwolfinger.at/ downtown URL:http://grapes-network.eu/event/final-open-conference-and-career-fair/ LOCATION:Johann Radon Institute for Computational and Applied Mathematics (RICAM)\, Altenberger Str. 69\, Linz\, 4040\, Austria ORGANIZER;CN="Josef Schicho":MAILTO:josef.schicho@ricam.oeaw.ac.at END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20230904T140000 DTEND;TZID=UTC:20230908T170000 DTSTAMP:20260613T034856 CREATED:20230613T181734Z LAST-MODIFIED:20230909T083220Z UID:2103-1693836000-1694192400@grapes-network.eu SUMMARY:Learning Week II: Industrial skills and advanced topics in ML DESCRIPTION:[slideshow_deploy id=’2136′] \nThe second Learning Week and advanced topics in ML workshop will take place physically in Barcelona from Monday\, September 4 to Friday\, September 8\, 2023. \nThe event will include a 2-day scientific workshop with courses on advanced topics in Geometric Learning. Confirmed speakers include Johannes Brandstetter (Microsoft)\, Henry Bucklow (ITI)\, Christoph Hofer (RISC-Software)\, Despoina Paschalidou (Stanford)\, Denys Plakhotnik (ModuleWorks)\, Panagiotis Kaklis (U. Strathclyde)\, Bhalaji Nagarajan (U. Barcelona)\, Maria Alberich and Franco Coltraro (Institut de Robotica i Informatica Industrial – Barcelona Tech). \nThe Learning week workshop will be about connections with the industrial workplace environment\, and how to transition there. \n[slideshow_deploy id=’2192′] \nRegistration\nRegistration is closed! \nLocation\nThe meeting will take place at the Institut de Matematiques of the University of Barcelona. \nGran Via de les Corts Catalanes\, 585\n08007 Barcelona \nAll talks and meetings will happen at Room T1\, on the top (second) floor of the Historic Building of the University of Barcelona\, on the premises of the Mathematics Faculty (http://www.mat.ub.edu/). \nThe activities on Wednesday 06/09 will happen in the neighborhood of “Barceloneta”. The closest metro station is Barceloneta (L4). \nWe will gather at 9.45 am at the main entrance of Museu d’Historia de Catalunya (https://goo.gl/maps/MJtTTRqUJM6dWV37A). By 10 am someone from The Collider will come and pick us and lead us into their facilities. It is important that we are all there by 10 am because we need the clearance from some of the workers there to get in\, and also you MUST BRING AN OFFICIAL IDENTITY DOCUMENT to fulfill the security measures of the building. \nThe visit will take around 2 hours\, and after that we will gather for lunch at the restaurant Can Ros (https://goo.gl/maps/Aj7ERbc91tmuLjFY9). Our appointment there is at 1 pm in case you decide to go directly to the restaurant. \nHow to Arrive \nThe Mathematics Faculty is located next to Plaça de la Universitat (University Square). The subway lines L1 (red) and L2 (purple) have stations at Plaça Universitat and many city buses pass through this area. See metro and bus networks (http://www.tmb.cat/en/home). \nThe Historic Building is at a short walking distance from Plaça de Catalunya\, the city’s nerve center. \nSchedule \n[last update: September 5]\nA pdf version of the schedule can be found here. All times are CEST.\nWhen available\, slides of the presentations can be downloaded by following the link that appers in the program. \n\n\n\n\n Monday\, Sep 4thTuesday\, Sep 5thWednesday\, Sep 6thThursday\, Sep 7thFriday\, Sep 8th\n\n\n\n\n 9:00-10:00P. Mazon\, \nM. MarsalaVisit to the ColliderHenry Bucklow (ITI)Johannes Brandstetter (Microsoft)\n\n\n 10:00-10:30Coffee break- // -Coffee breakCoffee break\n\n\n 10:30-11:30WP Meeting- // -Despoina Paschalidou (Stanford)Despoina Paschalidou (Stanford)\n\n\n 11:30-12:30WP Meeting- // -Christoph Hofer (RISC-Software)Maria Alberich / Franco Coltraro \n(Institut de Robotica i Informatica Industrial - Barcelona Tech)\n(until 1pm)\n\n\n 12:30-14:30Lunch breakLunch at Can Ros restaurant (13:30)Lunch breakLunch break\n\n\n 14:30-15:30Panagiotis Kaklis \n(U. Strathclyde)S. Khan\, A. Nair-Johannes Brandstetter (Microsoft)-\n\n\n 15:30-16:00Coffee breakCoffee break-Coffee break-\n\n\n 16:00-17:00C. Checa\, \nK. TertikasE. Hoxhaj\, \nA. Ramanantoanina-Denys Plakhotnik (ModuleWorks)-\n\n\n 17:00-18:00R. Fu\, T. ZoumpekasJ.M. Menjanahary\, \nK. Raval-Bhalaji Nagarajan (U. Barcelona)-\n\n\n 18:00-19:00Supervisory Board meeting [ login required]A. Georgiou\, A. Berzins-Educational Committee meeting-\n\n\n\n\n\nInvited talks ESR presentations\n \n\n \n María Alberich & Franco Coltraro (Universitat Politècnica de Catalunya) - Mathematical challenges in robotic manipulation of clothes. \n \n \n \n \nThe movement of a garment has infinite degrees of freedom. This originates the main difficulties for its robotic handling. Automated manipulation of clothing requires to forecast and understand its position while the clothing is grabbed. We will address these questions and we will show some solutions obtained within the Clothilde project at IRI (CSIC-UPC).\n \n\n \n\n \n Johannes Brandstetter (Microsoft) - Geometric Algebra Neural Networks in the era of GPT models. \n \n \n \n \nIn the era of GPT models\, one gets notoriously confronted with the question of how much inductive (geometric) bias can help when scaling up deep neural architectures. In this lecture\, we first contrast modern geometric deep learning approaches\, such as group equivariant learning\, with large-scale Transformer success stories. Secondly\, we introduce Clifford (geometric) algebras as an emerging paradigm within the deep learning community. To delve deeper\, we commence with a comprehensive overview of modern (plane-based) geometric algebra\, which is founded on the representation of isometries as elements within the Pin(p\, q\, r) group. Subsequently\, we propose the concept of group actions and Clifford group equivariant layers. Lastly\, we discuss the potential application of these layers when scaling up neural networks\, which\, in turn\, could potentially pave the way for the development of geometric GPTs in the future.\n \n\n \n\n \n Henry Bucklow (ITI) - Geometry reasoning using hybrid modelling. \n \n \n \n \nModern products are increasingly designed\, optimised\, and manufactured using geometric and physical models held on computer\, so-called digital twins. As these continue to grow in scale\, complexity and ambition\, gaps appear in the digital thread connecting these models\, where engineers must manually intervene\, and transform their geometry to suit each tool or analysis model. This talk will describe some of the geometry reasoning technology ITI has developed to address these gaps\, how hybrid geometry representations have become key to our approach\, and where the future opportunities might lie.\n \n\n \n\n \n Christoph Hofer (RISC-Software) - AI based prediction of finite element simulation results in mechanical engineering. \n \n \n \n \nIn this talk\, we explore AI methods for predicting stresses when the parameters describing the shape of the component change. We focus on two techniques\, one using mesh deformation and the other using image-based approaches using pretrained convolutional neural networks. We provide numerical results to predict the Van Mises stresses for a parameterized two-dimensional component.\n \n\n \n\n \n Panagiotis Kaklis (U. STrathclyde) - ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional GANs. \n \n \n \n \n In this work\, we introduce ShipHullGAN\, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) for the versatile representation and generation of ship hulls. We trained ShipHullGAN on a large dataset of 52\,591 physically validated designs from a wide range of existing ship types\, including container ships\, tankers\, bulk carriers\, tugboats\, and crew supply vessels. We developed a new shape extraction and representation strategy to convert all training designs into a common geometric representation of the same resolution\, as typically GANs can only accept vectors of fixed dimension as input. A space-filling layer is placed right after the generator component to ensure that the trained generator can cover all design classes. During training\, designs are provided in the form of a shape-signature tensor (SST) which harnesses the compact geometric representation using geometric moments that further enable the inexpensive incorporation of physics-informed elements in ship design. We have shown through extensive comparative studies and optimisation cases that ShipHullGAN can generate designs with augmented features resulting in versatile design spaces that produce traditional and novel designs with geometrically valid and practically feasible shapes.\n \n\n \n\n \n Bhalaji Nagarajan (U. Barcelona) - Self-supervised Fine-grained Food Recognition. \n \n \n \n \n Food recognition presents a formidable challenge in computer vision and machine learning\, stemming from the intrinsic complexity of food images. The complexity is characterized by high inter-class similarity and intra-class variability\, the fine-grained nature of food classes\, visual differences arising from different cooking styles\, lack of distinctive spatial layouts and rigid structures\, and randomly distributed ingredients across the food platter. As is the case with any deep learning problem\, the training data plays a pivotal role in food recognition. In this talk\, we address two data-centric questions that deepen our comprehension of food data. First\, we investigate the issue of data labelling by harnessing self-supervised learning algorithms. We present “All4One”\, a novel neighbour-based contrastive SSL approach that employs “centroids” generated through a self-attention mechanism to reduce the distance between different neighbour representations. Second\, we tackle the challenge posed by the fine-grained nature of classes through subset learning-based methods. We introduce “Dining on Details”\, an expert learning framework for food classification that incorporates lexical information extracted using LLMs. Both proposed solutions shed light on critical aspects of data-centred food recognition and pave the way for more accurate and robust food recognition models.\n \n\n \n\n \n Despoina Paschalidou (Stanford U.) - 1st Talk: Learning to generate and manipulate 3D objects and scenes. \n \n \n \n \nThe ability to generate realistic and diverse 3D shapes has the potential to significantly accommodate the workflow of artists and content creators and potentially enable new levels of creativity through “generative art”. However\, despite the impressive progress in generative models that can generate plausible 3D shapes of high fidelity\, an important aspect of the generation process is to enable control on what parts of the generated object should change. Therefore\, having a basic understanding of the decomposition of an object into parts facilitates controlling what to edit. In the first part of my talk\, I will present PartNeRF\, which is a part-aware generative model for editable 3D shape synthesis that is trained using posed images. The key idea is to generate objects as a set of locally defined NeRFs\, that emerge automatically during training. Our formulation enables part-level control\, hence permitting several editing operations such as rigid and non rigid transformations on parts\, mixing parts from different objects etc. Although PartNeRF\, can reliably edit the appearance and the shape of the generated instance\, it is an unconditional generative model. However\, being able to generate realistic 3D geometries from versatile user inputs is a crucial task that can unlock numerous editing capabilities. To address this\, we introduced PASTA\, which is an autoregressive transformer architecture for controllable shape generations that focuses on yielding detailed geometries. PASTA comprises two main components: An autoregressive transformer that generates objects as a sequence of cuboidal primitives and a blending network\, implemented with a transformer decoder that composes the sequences of cuboids and synthesizes high quality meshes for each object. Our model is trained in two stages: First we train our autoregressive generative model using only annotated cuboidal parts as supervision and next\, we train our blending network using explicit 3D supervision\, in the form of watertight meshes. Our model\, trained with various conditionings\, can be used to perform shape generation from diverse inputs e.g. from scratch\, from a partial object\, from text and images\, as well size-guided generation. Moreover\, as PASTA considers the underlying part-based structure of a 3D object\, we are able to select a specific part and produce shapes with meaningful variations of this part. Finally\, I will present two works for controllable scene synthesis. First\, I will talk about ATISS\, which is a novel autoregressive transformer architecture for creating diverse and plausible synthetic indoor environments that poses scene synthesis as sequence generation and generates rooms as unordered sets of objects. Next\, I will also talk about our recent work\, CC3D\, which is a conditional generative model for scene synthesis that does not require 3D labelled bounding boxes as supervision and instead can be trained using only posed images.\n \n\n \n\n \n Despoina Paschalidou (Stanford U.) - 2nd Talk: Learning meaningful representations for understanding 3D objects and scenes. \n \n \n \n \nWithin the first year of our life\, we develop a common-sense understanding of the physical behavior of the world\, which relies heavily on our ability to properly reason about the arrangement of objects in a scene. While this seems to be a fairly easy task for the human brain\, computer vision algorithms struggle to form such high-level reasoning. Therefore\, the research community shifted their attention to the development of primitive-based methods that seek to represent objects as semantically consistent part arrangements. However\, due to the simplicity of existing primitive representations\, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. In the first part of my talk\, I will introduce several compositional representations that have been explored in the literature. I will address the trade-off between reconstruction quality and number of parts and present Neural Parts\, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. Since a homeomorphism does not impose any constraints on the primitive shape\, our model effectively decouples geometric accuracy from parsimony and as a result captures complex geometries with an order of magnitude fewer primitives. Next\, I will present our recent work ALTO\, that can produce high-fidelity reconstruction of implicit 3D surfaces from noisy point clouds. Our key idea is to alternate back and forth between point and grid latents before converging to a final feature representation that is easy to decode. ALTO is able to yield high-quality 3D meshes while being up to 10 times faster than prior works. Finally\, I will present COPILOT\, a transformer-based architecture capable of performing collision prediction and localization simultaneously given either multi-view or single-view egocentric video observations. COPILOT accumulates information across multi-view inputs through a novel 4D space-time-viewpoint attention mechanism and provides rich information regarding imminent collisions.\n \n\n \n\n \n Denys Plakhotnik (ModuleWorks) - AI in production engineering. \n \n \n \n \nNowadays\, production of goods and industrial components relies on a mixture of digital and classical fabrication steps. AI and ML seem to be right tools to automate the existing workflows to reduce involvement of human beings and optimize the outcome of the manufacturing industry. In this presentation\, the audience will be immersed in the existing challenges and opportunities that can be reached with the AI. Generic industrial examples and AI try-outs at ModuleWorks will be presented.\n \n\n \n\n\n \n Carles Checa (Athena RC) - The regularity: a computational invariant. \n \n \n \n \nIn my talk\, I will try to motivate the relevance of the Castelnuovo-Mumford regularity as an invariant that governs the computations with polynomial systems. Namely\, I will discuss the relation of this invariant with elimination matrices\, Groebner bases\, resultants and incremental algorithms for solving polynomial systems. I will also deal with the possible extensions of this invariant to multigraded and sparse systems\, and motivate it with many examples coming from applications.\n \n\n \n\n \n Konstantinos Tertikas (Athena RC) - Generating 3D Shapes Using Limited Supervision. \n \n \n \n \n Impressive progress in generative models and implicit representations gave rise to methods that can generate 3D shapes of high quality. However\, being able to locally control and edit shapes is another essential property that can unlock several content creation applications. Local control can be achieved with part-aware models\, but existing methods require 3D supervision and cannot produce textures.\nIn the first part of the talk\, we present PartNeRF\, a novel part-aware generative model for editable 3D shape synthesis that does not require any explicit 3D supervision. Our model generates objects as a set of locally defined NeRFs\, augmented with an affine transformation. This enables several editing operations such as applying transformations on parts\, mixing parts from different objects etc. Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity\, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.\nExisting 3D-aware generative methods – including PartNeRF – are trained using synthetic data\, and require multiple views per object instance and known camera poses. However\, from a user’s perspective\, it would be extremely beneficial to have the ability to generate 3D shapes using only in-the-wild images. In the second part of the talk\, we focus on the task of generating 3D shapes from in-the-wild images and present our preliminary results.\n \n\n \n\n \n Amrutha Balachandran (U. Barcelona) - Characterization of Degenerate Cubic Plane Curves. \n \n \n \n \nCharacterizes degenerate cases of cubic plane curves by practicable formulas in terms of the coefficients of the polynomials describing the curve. We will use minors of the well-known matrices associated with the cubic curves such as Hessian matrix.\n \n\n \n\n \n Athanasios Zoumpekas (U. Barcelona) - Unsupervised 3D Scene Segmentation via Object-Level Generative Modeling. \n \n \n \n \nIn the domain of computer vision\, decomposing 3D scenes into object instances is a fundamental challenge. In addition\, achieving accurate segmentation of 3D scenes without the laborious task of annotating data is paramount for applications spanning robotics to augmented reality. This presentation introduces a paradigm-shifting approach to unsupervised 3D scene segmentation\, driven by object-level generative modeling that transcends the necessity for scene-level supervision. By parsing the point cloud of a 3D scene and utilizing the object likelihood of a variational autoencoder model\, we discover and segment the objects inside the scene. The empirical validation highlights the effectiveness of our unsupervised 3D scene segmentation approach\, paving the way for profound advancements in 3D scene understanding\, all without being tethered to the traditional way of exhaustive annotations.\n \n\n \n\n \n Pablo González Mazón (Inria) - Weak $(1-epsilon)$-nets for polynomial superlevel sets. \n \n \n \n \nWe present new results about the partitioning of measures with polynomials. Namely\, for any Borel probability measure $\mu$ on $\R^n$ there exists a set $X\subset \R^n$ of $n+1$ points such that any $n$-variate quadratic polynomial $P$ that is nonnegative on $X$ (i.e. $P(x)\geq 0$\, for every $x \in X$) satisfies $\mu\{P\geq 0\}\geq \frac{2}{(n+1)(n+2)}$.\nMore generally\, given an absolutely continuous probability measure $\mu$ on $\R^n$ and $D\leq 2k$\, for every $\delta>0$ there exists a set $X\subset \R^n$ with $|X|\leq \binom{n+2k}{n}-n-1$ such that any $n$-variate polynomial $P$ of degree $D$ that is nonnegative on $X$ satisfies $\mu\{P\geq 0\}\geq \frac{1}{\binom{n+2k}{n}+1} – \delta$. These statements are analogues of the celebrated \emph{centerpoint theorem}\, which corresponds to the case of linear polynomials. Our results follow from new estimates on the Carath\’eodory numbers of real Veronese varieties\, or alternatively\, from bounds on the convex symmetric rank of real symmetric tensors.This is a joint work with Alfredo Hubard and Roman Karasev.\n \n\n \n\n \n Michelangelo Marsala (Inria) - Geometrically Smooth Splines on Meshes. \n \n \n \n \nGeometrically smooth spline functions are piecewice polynomial functions defined on a mesh\, that satisfy properties of differentiability across shared edges; their unstructured nature provides them a wide range of application. We start introducing a G1 spline construction defined by means of smoothing masks on quad meshes approximating the well-known Catmull-Clark surface. Furthermore\, we provide a set of G1 basis functions\, describe their properties and analyze their space\, to be applied in point data fitting problems and Isogeometric Analysis simulations.\n \n\n \n\n \n Eriola Hoxhaj (JKU) - Reconstruction of Surfaces and Planar Maps from their Branching Curve. \n \n \n \n \nThe task of identifying an algebraic surface from a single branching curve (apparent contour) can be simplified by recovering a homogeneous equation in four variables from its branching curve (which is defined by the discriminant).\nCurrently\, successful reconstructions have been achieved for smooth surfaces\, surfaces with ordinary singularities\, ruled surfaces\, and the latest work focuses on reconstructing Darboux cyclides.\nThere is used the fact that the Darboux Cyclides have a singularity along the absolute conic in order to recognize them up to Euclidean similarity transformations.\nBy applying the same concept\, it becomes feasible to reconstruct a planar map from a given branching curve. The stepping stone is the ramification curve\, which is obtained as the linear normalization of the branching curve\, which lies on a parametric surface. The composition of the parametrization with linear normalization yields a planar map that shares the same branching curve as the parametric surface.\n \n\n \n\n \n Arturs Berzins (SINTEF) - Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision. \n \n \n \n \nA neural network consisting of piecewise affine building blocks\, such as fully-connected layers and ReLU activations\, is itself a piecewise affine function supported on a polyhedral complex. This complex has been previously studied to characterize theoretical properties of neural networks\, but\, in practice\, extracting it remains a challenge due to its high combinatorial complexity. A natural idea described in previous works is to subdivide the regions via intersections with hyperplanes induced by each neuron. However\, we argue that this view leads to computational redundancy. Instead of regions\, we propose to subdivide edges\, leading to a novel method for polyhedral complex extraction. A key to this are sign-vectors\, which encode the combinatorial structure of the complex. Our approach allows to use standard tensor operations on a GPU\, taking seconds for millions of cells on a consumer grade machine. Motivated by the growing interest in neural shape representation\, we use the speed and differentiability of our method to optimize geometric properties of the complex.\n \n\n \n\n \n Shahroz Khan (U. Strathclyde\, now: BAR TECHNOLOGIES) - Leveraging Ship Hull Design and Optimisation with Deep Generative Models. \n \n \n \n \nIn this work\, we introduce ShipHullGAN\, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) for the versatile representation and generation of ship hulls. At a high level\, the new model intends to address the current conservatism in the parametric ship design paradigm\, where parametric modellers can only handle a particular ship type. We trained ShipHullGAN on a large dataset of 52\,591 physically validated designs from a wide range of existing ship types\, including container ships\, tankers\, bulk carriers\, tugboats\, and crew supply vessels. We developed a new shape extraction and representation strategy to convert all training designs into a common geometric representation of the same resolution\, as typically GANs can only accept vectors of fixed dimension as input. A space-filling layer is placed right after the generator component to ensure that the trained generator can cover all design classes. During training\, designs are provided in the form of a shape-signature tensor (SST) which harnesses the compact geometric representation using geometric moments that further enable the inexpensive incorporation of physics-informed elements in ship design. We have shown through extensive comparative studies and optimisation cases that ShipHullGAN can generate designs with augmented features resulting in versatile design spaces that produce traditional and novel designs with geometrically valid and practically feasible shapes.\nVideo abstract: https://youtu.be/LT9Z52vBgzI\n \n\n \n\n \n Alexandros Georgiou (USI) - On Universal Approximation Theorems: New from old. \n \n \n \n \nUniversal approximation theorems are results that demonstrate the density of a certain class of functions within a space of interest. We will begin our discussion with the early works of Cybenko\, Hornik\, and others\, on feed-forward neural networks\, up to more recent results that go beyond the Euclidean setting. Subsequently\, we will illustrate how we can use existing universal approximation theorems to prove new ones\, even in the light of a certain kind of symmetry.\n \n\n \n\n \n Andriamahenina Ramanantoanina (USI) - Shape control tools for curve design. \n \n \n \n \nWe discuss the classical methods in curve design such as Bezier and B-Spline. We also discuss some extension to the periodic case and other recent and notable alternatives. The common properties of these tools are that even though they are very intuitive tools\, they lack direct control over a curve. On the other hand\, the Barycentric rational form can be used to complement the previous methods\, since due to the interpolatory property\, we can force the curve to pass through specific points. We describe the correspondence of the classical method and the interpolation method to offer new editing possibilities of a curve.\n \n\n \n\n \n Jean Michel Menjanahary (Vilnius U.) - A recognition procedure for Dupin cyclides and its applications. \n \n \n \n \nA computational result about recognition of any implicit form of Dupin cyclides will be presented. Applications will include: singularity investigation\, illustration of properties of Dupin cyclides suggested in a classical paper of G. Darboux\, and investigation of Dupin orthogonal coordinate systems.\n \n\n \n\n \n Krunal Raval (UTV) - Adaptive Refinement through Non-Uniform Polynomial degree LR B-Splines in Isogeometric Analysis. \n \n \n \n \nLocally Refined B-splines (LR B-splines) [Dokken et al.\,Polynomial splines over locally refined box-partitions\, CAGD\, 2013] have proven to be a very flexible and powerful framework in several applications\, such as\, geometric modeling\, data analysis\, visualization and numerical simulation. In this work we extend LR B-splines to allow for non-uniform polynomial degree\, where we combine degree elevation with local h-refinement\, resulting in local k-refinement\, that is\, increasing the polynomial degree as well as the smoothness at newly introduced knot lines. The non-uniform degree LR B-splines are scaled by weights to maintain the nestedness of the resulting space and the non-negative partition of unity giving the convex hull property\, which can be computed by means of certain two-scale relationship. In this talk\, we discuss the theoretical properties of the new spline spaces ensuring the fundamental properties like local support\, (scaled) partition of unity\, etc.\, and their application in several 2D model problems\, illustrating the efficacy of the proposed adaptive refinement methodology in the context of isogeometric analysis.\n \n\n \n\n \n Rao Fu (GeometryFactory) - Shape Reconstruction from Raw Point Sets. \n \n \n \n \nShape reconstruction from raw point sets is a pivotal task in the realms of computer graphics and computer vision. This presentation presents the intricate journey of transforming unstructured point data into coherent 3D representations of objects. We explored both explicit and implicit methods to reconstruct shapes. For explicit reconstruction\, we present Bézier primitive segmentation on 3D point clouds. Different from previous primitive segmentations that treat different primitives separately\, we take inspiration from Bézier decomposition on NURBS models and transfer it to guide point cloud segmentation casting off primitive types. Experiments show superior performance over previous work in terms of segmentation\, with a substantially faster inference speed. For implicit reconstruction\, we present a method for reconstructing an isotropic surface triangle mesh from an unoriented point cloud whose density adapts to an estimate of the local feature size (LFS). Instead of performing dense reconstruction followed by remeshing\, our approach reconstructs both an implicit function and an LFS-aware mesh sizing function\, which are then used for producing the final LFS-aware reconstructed mesh. Our approach’s added value is generating isotropic meshes directly from 3D point clouds with an LFS-aware density. Our experiments demonstrate robustness to noise\, outliers\, and missing data. URL:http://grapes-network.eu/event/learning-week-ii/ LOCATION:University of Barcelona\, Gran Via de les Corts Catalanes 585\, Barcelona\, 08007\, Spain ORGANIZER;CN="Carlos D'Andrea":MAILTO:cdandrea@ub.edu END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20230214 DTEND;VALUE=DATE:20230218 DTSTAMP:20260613T034856 CREATED:20221029T074513Z LAST-MODIFIED:20230223T183700Z UID:1878-1676332800-1676678399@grapes-network.eu SUMMARY:Software & Industrial workshop II and ESR Days DESCRIPTION:Athena statue at the Academy of Athens\n \n \n \n \n \n Monastiraki\n \n \n \n \n \n Lecabetus hill\n \n \n\nThe Software and Industrial Workshop II (SIW2) is “GRAPES developer days”\, allowing for senior researchers and ESRs to partake in coding sessions\, completing small coding tasks and learning from each other. These sessions aim to facilitate knowledge transfer towards first solutions of real-world problems posed by our industrial partners.\nΙnvited speakers include: \n\nChristoph Hofer (RISC-Software)\, \nXMANAI team – Theodore Dalamagas\, Eleni Lavasa\, Vasilis Gkolemis (Athena RC)\, \nLeonidas Guibas (Stanford University)\, \nAndré Lieutier (Geometry Engineering\, Dassault Systèmes)\, \nFrancesco Patrizi (University of Florence)\,\nClaudio Mancinelli (University of Genoa)\n\nThe ESR Days is an important aspect of networking and training\, collocated with SIW2. The ESRs\, through their organisation\, coordinated by their representatives and assisted by the Coordinator and the organisers of SIW2\, shall have to make choices and assign roles to each other. They shall choose GRAPES and external speakers\, shall organise oral or poster presentations by themselves\, undertake public engagement activities\, and shall interact so as to foster ties useful for their future careers. \n[slideshow_deploy id=’2045′] \nLocation\nThe meeting will take place physically at the Fresh Hotel in downtown Athens\, see the map below. The hotel is close to the Athens City Hall and within walking distance form the Omonia square and Monastiraki square and the corresponding metro stations. Lunches and coffee breaks will be served on site. \nFresh Hotel Athens\n26 Sofokleous Str.\n10552 Athens\, Greece\nT. +30 210 5248511\nF. +30 210 5248517\ninfo@freshhotel.gr\nwww.freshhotel.gr \nGoogle maps: https://goo.gl/maps/BnrsoqrkggZ1DJS29 \n\nWorkshop Dinner\nThe workshop dinner will take place at the GH Atttikos restaurant on Wednesday\, February 15th\, at 20:00. The restaurant is located near the Acropolis\, at a walking distance (2km\, 20′-30′) from the workshop venue\, see this map for alternative routes. You can also take the metro red line from Omonia station and get out at the Acropolis station. From there it is a 10 minute walk\, see this map. \nGH Attikos Restaurant\n7 Garivaldi Str\, 11742 Athens\nT. +30 210 9215256\ninfo@ghattikos.gr \n\nRegistration\nRegistration is now closed. The list of participants can be found here. \n\nSchedule of the event\nThe event will start on Tuesday morning and will close on Friday early afternoon. A pdf version of the schedule can be found here. \n\n\n\n\n Tuesday\, Feb 14thWednesday\, Feb 15thThursday\, Feb 16thFriday\, Feb 17th\n\n\n\n\n 9:00-9:30Registration---\n\n\n 9:30-10:30A. Lieutier - From topological inference to meshing algorithms-RISC-Software / C. Hofer - Numerical and automatic differentiation XMANAI team - Interpretable Machine Learning \n\n\n 10:30-11:00Coffee breakCoffee breakCoffee breakCoffee break\n\n\n 11:00-12:30A. Lieutier - From topological inference to meshing algorithmsParallel WP sessionsRISC-Software / C. Hofer - Numerical and automatic differentiationXMANAI team - eXplainable Artificial Intelligence Application in Metrology\n\n\n 12:30-14:00Lunch breakLunch breakLunch breakLunch break\n\n\n 14:00-15:00ESR Days: C. Mancinelli - Vector Graphics on Discrete Surfaces\, (pdf version)ESR days: F. Patrizi - An Algorithmic Introduction to LR B-splinesESR Days: P. Mazon\, C. Checa\, E. Hoxhaj\, J.M. Menjanahary-\n\n\n 15:00-15:30Coffee breakCoffee breakCoffee break-\n\n\n 15:30-16:30ESR Days: A. Berzins\, R. Fu\, A. NairESR Days: K. Tertikas\, M. Marsala\, T. ZoumpekasESR Days: M. Ramanantoanina\, K. Raval\, S. Khan\, A. Georgiou-\n\n\n 16:30-17:30Educational Committee meetingSupervisory Board meeting--\n\n\n 17:30-18:00-Coffee break-\n\n\n 18:00-19:00-L. Guibas - Invited talk--\n\n\n 20:00-Workshop dinner--\n\n\n\n\n\nInvited talks and tutorials\nSoftware & Industrial Workshop\n\n\n \n André Lieutier (Geometry Engineering) - From topological inference to meshing algorithms \n \n \n \nAndré Lieutier (Geometry Engineering) – From topological inference to meshing algorithms\nIn this tutorial we consider the general problem of retrieving a topologically correct model from partial geometric information. Applications areas span from automated 3D scene faithful reconstruction from scanned points clouds to abstract submanifolds inference from high dimensional data in the context of machine learning.\nIn a first part\, the tutorial will recall several topological notions and consider their possible inference from data. In a second part\, we focus on the computation of simplicial complexes homeomorphic to manifolds known through point samples\, in other words\, manifolds triangulation algorithms with topological guarantees.\n \n\n \n\n \n Leonidas Guibas (Stanford university) - Learning to Vary and Varying to Learn \n \n \n \nLeonidas Guibas (Stanford university) – Learning to Vary and Varying to Learn\nVariations in how a 3D object or scene is presented to a learning algorithm\, for example the coordinate system chosen\, can significantly impact the learned outcome. At the same time\, the space of intrinsic variations of the scene itself is intimately connected with the semantics of the scene and the continuous or discrete attributes we are trying to learn. Certain variations may be just nuisance while others crucial to the task at hand. The talk highlight a few efforts that address this issue of disentangling how variations of a scene or of its presentation affect learning tasks\, aiming to show the value of low dimensional parametrizations of the semantic tangent space of object and scene representations\, towards both analysis and synthesis learning tasks. \nA first vignette looks at how to design neural architectures that are invariant or equivariant to spatial transforms like rigid motions that affect scene presentation and introduces the vector neuron machinery for neural components that operate on vectors and not scalars. In the second vignette we look at how to shape neural scene representations such as NeRFs to better reflect the compositional structure of the scene by doing contrastive learning based on co-variation patterns between scene points or pixels (in scene views) with high mutual information. These learned “resonances” allow efficient propagation of sparse annotations or semantically coherent scene edits. Finally\, in the third vignette we look at how language that differentiates related object geometries can be used for segmentation as well as for the generation of object variants based on written/spoken instructions.\n \n\n \n\n \n Christoph Hofer (RISC-Software) - Numerical and automatic differentiation \n \n \n \nChristoph Hofer (RISC-Software) – Numerical and automatic differentiation \nIn this tutorial session\, we will investigate different ways to program the computer to calculate derivatives of functions. To be precise we consider difference-quotients\, the complex step\, and automatic/algorithmic differentiation methods. We discuss their advantages\, disadvantages\, and limitations. Besides the algorithmic realization\, we will also look at their mathematical properties. The different algorithms and examples will be implemented in small programming exercises. \nThe preferred language for the exercises is python\, but in principle the participants are free to use any language as long as it provides operator overloading\, graphs of functions/line charts can be plotted\, and complex numbers are supported. For python various plotting libraries exist\, a common one is plotly (https://plotly.com/python/). A very basic understanding of python numpy is advantageous. The participants\, who use python should have the packages numpy\, plotly and pandas installed. I advise to use PyCharm as an IDE.\n \n\n \n\n \n XMANAI team ( Theodore Dalamagas\, Eleni Lavasa\, Vasilis Gkolemis - Athena RC) - XAI application in Industrial Metrology \n \n \n \nXMANAI team (Theodore Dalamagas\, Eleni Lavasa\, Vasilis Gkolemis – Athena RC) – XAI application in Industrial Metrology — SLIDES: Interpretable Machine Learning\, eXplainable Artificial Intelligence Application in Metrology\nNowadays\, quality Control of manufactured parts (e.g.\, automotive\, aeronautical and energy sectors) is performed with advanced optical technology (3D laser scanning) in the Industry 4.0 era to achieve dimensional quality requirements and tolerances with high precision and in minimum time. Each part under metrological study is captured in a 3D Point Cloud\, which is subsequently analyzed with domain-specific software to obtain the dimensional measurement and estimate the associated accuracy of the measurement (tolerance). In this tutorial we examine the data science applications for industrial metrology scenarios: starting from the manipulation of Point Cloud data\, moving on to the prediction of an instrument’s accuracy in point capturing using popular Machine Learning algorithms\, and closing with the application of post-hoc explainability techniques to interpret the predictions of the ML models. Our goal is to showcase the benefits explainable AI (XAI) has to offer in complex industrial settings such as Industrial Metrology. \nTutorial outline: \n\nPart A (1h)\n\nIntroduction to Explainable AI and the XMANAI research project (5 min)\nIndustrial Metrology: the Use Case of UNIMETRIK pilot in XMANAI (15 min)\nLab#1: Point Cloud analysis and feature extraction (30 min)\nSoftware: Python3 / numpy\, pandas\, jupyter notebook\, open3d\nDiscussion (10min)\n\n\nPart B (1.5h)\n\nLab#2: Building a predictor for point capturing accuracy (30 min)\nSoftware: Python3 / numpy\, pandas\, jupyter\, scikit-learn\nDiscussion: Quantitative & Qualitative model evaluation (15 min)\nLab#3: Explainability study of the predictive models (30 min)\nSoftware: Python3 / scikit-learn (permutation importance\, pdp)\, SHAP\nDiscussion: Assessing the quality/impact of explanations (15 min)\n\n\n\n \n\n \n \nESR Days Workshop\n \n\n \n Claudio Mancinelli (University of Genoa) - Vector Graphics on Discrete Surfaces \n \n \n \nClaudio Mancinelli (University of Genoa) – Vector Graphics on Discrete Surfaces\, (pdf version)\nSoftware packages for the generation of vector graphics in 2D are available in many forms nowadays. The concept of geometric primitive is at the core of this technology. Essentially\, geometric primitives are basic geometric shapes that can be used and combined to generate more complex ones. Such primitives are defined in a plane endowed with the Euclidean metric\, together with a Cartesian coordinate system\, and the algorithms to generate them are based on this structure. \nIn this talk\, we will start by describing the theoretical and computational challenges that one needs to overcome when trying to bring this technology onto a (discrete) surface. The theoretical ones\, are mainly due to the fact that we can no longer rely on the Euclidean metric. From a computational point of view instead\, one needs to ensure the correctness\, robustness and efficiency of its implementation\, in order to reproduce the behaviour of the 2D drawing systems. We will then present some examples of publicly available implementations in which both of these problems have been successfully addressed.\n \n\n \n\n \n Arturs Berzins (SINTEF) - Neural Implicit Shape Editing using Boundary Sensitivity \n \n \n \nArturs Berzins (SINTEF) – Neural Implicit Shape Editing using Boundary Sensitivity.\nNeural fields are receiving increased attention as a geometric representation due to their ability to compactly store detailed and smooth shapes and easily undergo topological changes. Compared to classic geometry representations\, however\, neural representations do not allow the user to exert intuitive control over the shape. Motivated by this\, we leverage boundary sensitivity to express how perturbations in parameters move the shape boundary. This allows to interpret the effect of each learnable parameter and study achievable deformations. With this\, we perform geometric editing: finding a parameter update which best approximates a globally prescribed deformation. Prescribing the deformation only locally allows to deform the rest of the shape according to some prior\, such as semantics or deformation rigidity. Different to previous efforts\, our method is model-agnostic and can be applied to any pre-trained NN and update it in-place. Furthermore\, we show how boundary sensitivity helps to optimize and constrain objectives (such as surface area and volume)\, which are difficult to compute without first converting to another representation\, such as a mesh.\n \n\n \n\n \n Rao Fu (GF) - Local feature size adaptive surface reconstruction from unoriented point sets \n \n \n \nRao Fu (GF) – Local feature size adaptive surface reconstruction from unoriented point sets\nThis work seeks to design a surface reconstruction algorithm from unoriented point sets by adapting to the local feature size. The method has three steps: constructing an envelope\, signing the envelope\, and adaptive meshing. First\, a Delaunay refinement process produces the envelope from the iso-level of the unsigned distance function field. Afterward\, we use a globally optimized solver to solve the sign of the envelope by a signing guess from the edges. Finally\, we use another Delaunay refinement process to mesh the target shape by a sizing function adaptive to the local feature size. This method is easy to implement and can guarantee the output of an isotropic mesh in a single pass. Experiments prove that our algorithm outperforms two-stage algorithms.\n \n\n \n\n \n Amrutha Balachandran Nair (Inria) - Mu-Basis of Parametrizations of Surfaces \n \n \n \nAmrutha Balachandran Nair (Inria) – Mu-Basis of Parametrizations of Surfaces \nMu-basis is the basis of syzygy module of a parametrization from which we can retrieve the parametrization back. We will be looking at certain parametrizations in two variables\, that describes surfaces and how to find mu-basis. In particular we will be looking at translational surfaces and generalized surfaces of revolution. Translational surfaces\, which are surfaces generated by two rational curves by sliding one along the other. Generalized surfaces of revolution are surfaces are generated by rotating a rational space curve around another curve.\n \n\n \n\n \n Konstantinos Tertikas (Athena RC) - Generating Part-Aware Editable 3D Shapes without 3D Supervision \n \n \n \nKonstantinos Tertikas (Athena RC) – Generating Part-Aware Editable 3D Shapes without 3D Supervision \nImpressive progress in generative models and implicit representations gave rise to methods that can generate 3D shapes of high quality. However\, being able to locally control and edit shapes is another essential property that can unlock several content creation applications. Local control can be achieved with part-aware models\, but existing methods require 3D supervision and cannot produce textures. In this work\, we devise a novel part-aware generative model for editable 3D shape synthesis that does not require any explicit 3D supervision. Our model generates objects as a set of locally defined NeRFs\, augmented with an affine transformation. This enables several editing operations such as applying transformations on parts\, mixing parts from different objects etc. To ensure distinct\, manipulable parts we enforce a hard assignment of rays to parts that makes sure that the color of each ray is only determined by a single NeRF. As a result\, altering one part does not affect the appearance of the others. Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity\, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.\n \n\n \n\n \n Andriamahenina Ramanantoanina (USI) - TBA \n \n \n \nTBA\n \n\n \n\n \n Thanasis Zoumpekas (UB) - Temporal 3D Point Cloud Processing and Understanding \n \n \n \nThanasis Zoumpekas (UB) – Temporal 3D Point Cloud Processing and Understanding\nPoint clouds have emerged as a crucial data type in the field of 3D analysis\, owing to their ability to provide a comprehensive and accurate representation of real-world environments. They are utilized in various areas such as autonomous driving\, geoscience\, construction\, manufacturing\, and heritage preservation. However\, analyzing large and dynamic point clouds poses several challenges\, such as dealing with noise\, missing data\, and temporal consistency. This talk presents some of our studies on processing and understanding temporal and static 3D point clouds\, with a focus on geoscience and manufacturing applications. The studies include a proposed framework for rockfall detection\, a novel application of temporal 3D point clouds in manufacturing for machining tool identification\, and an ongoing study on unsupervised 3D detection of complex geometrical shapes in 3D point cloud scenes both indoors and outdoors.\n \n\n \n\n \n Francesco Patrizi (University of Florence) - An Algorithmic Introduction to LR B-splines \n \n \n \nFrancesco Patrizi (University of Florence) – An Algorithmic Introduction to LR B-splines\nIn this tutorial we shall introduce LR B-splines. These are generalization of B-splines over locally refined quadrilateral meshes and are exploited for a wide range of applications in Computer Aided Design (CAD) and Computer Aided Engineering (CAE)\, such as isogeometric analysis and quasi-interpolation\, in particular for bathymetry and terrain datasets.\nThere are several ways to introduce the LR B-splines. In this tutorial we will focus on an algorithmic approach: provided an initial set of tensor B-splines on a coarse tensor mesh we shall see the procedure to generate the LR B-splines while updating such mesh with local insertions. \nNext\, we shall see the main refinement algorithms for LR B-splines\, i.e.\, the procedures that automatically generate LR B-splines on the regions where they are needed to reduce the error in some sense. Namely\, we will see 6 strategies: the minimum span\, the full span\, the structured mesh\, the non-nested-support-structured mesh\, the hierachical mesh and the effective grading refinements.\nFinally\, if we have time\, we should address the linear dependence problem and how to solve it with the peeling algorithm.\n \n\n \n\n \n Eriola Hoxhaj (JKU) and Jean Michel Menjanahary (VU) - Reconstruction of Darboux cyclides from a single view apparent contour \n \n \n \nEriola Hoxhaj (JKU) and Jean Michel Menjanahary (VU) – Reconstruction of Darboux cyclides from a single view apparent contour\nThe task of recognizing an algebraic surface from its apparent contour can be reduced to the recovering of a homogeneous equation in four variables from its discriminant. We present a reconstruction algorithm that applies to Darboux cyclidic surfaces up to Euclidean transformation similarities.\n \n\n \n\n \n Carles Checa (Athena RC) and Pablo González-Mazón (Inria) - Approximating swept volumes with Bezier patches \n \n \n \nCarles Checa (Athena RC) and Pablo González-Mazón (Inria) – Approximating swept volumes with Bezier patches\nThe computation of the volume swept by a tool along a trajectory is an important step in the simulation of a machining process. One approach for this computation consists of sampling the surface of the tool with multiple points at different instants of the motion\, and fitting the resulting point cloud with an approximate surface using implicit functions. In our work\, we describe the pipeline of this computation with several types of volume representations. Namely\, we present an approach based on Bezier patches. In this approach\, the machining tool is given as a point cloud that is fitted by a curvilinear mesh\, i.e.\, it is described by Bezier patches. The computation of the swept volume is then performed using algebraic methods over the polynomial parametrizations given by these patches. Finally\, we use a routine of ray-tracing with elimination matrices that can be used for both visualization and point cloud reconstruction.\n \n\n \n\n \n Michelangelo Marsala (Inria) - Point Cloud Data Fitting via G1 Smooth Spline Basis Functions \n \n \n \nMichelangelo Marsala (Inria) – Point Cloud Data Fitting via G1 Smooth Spline Basis Functions\nGeometrically smooth spline functions are piecewice polynomial functions defined on a mesh\, that satisfy properties of differentiability across shared edges; their unstructured nature provide them a wide range of application like\, for example\, the point data fitting. Point cloud fitting is a very important topic especially in CAD theory\, which permits in fact to translate CAD data into Bézier representation. In the presentation we consider G1splines on quadrangular mesh defined with quadratic gluing data function along shared edges. We will describe shortly their construction\, their properties\, analyze their space\, and provide dimension formula. After having showed how to construct an efficient basis for the considered space\, numerical results will be presented to illustrate the quality of the fitting.  \n \n\n \n\n \n Krunal Raval (UTV) - Adaptive isogeometric analysis based on Tchebycheffian splines \n \n \n \nKrunal Raval (UTV) – Adaptive isogeometric analysis based on Tchebycheffian splines\nTchebycheffian splines are piecewise smooth functions with pieces in Tchebycheff spaces\, the natural generalization of polynomial spaces. Under suitable assumptions\, Tchebycheffian splines can be represented in terms of B-spline like basis functions\, called Tchebycheffian B-splines\, with structural similarities and all fundamental properties of classical polynomial B-splines (compact support\, non-negativity\, partition of unity\, etc.). Tchebycheffian splines offer a huge flexibility compared to classical polynomial splines: they are equipped with parameters that can be selected according to a problem-oriented strategy\, taking into account the geometrical and/or analytical issues of the specific addressed problem.\nWhile the tensor-product approach can easily build multivariate splines\, they lack adequate adaptive local refinement\, which could be important in both geometric modelling and numerical simulation. This triggered the interest in alternative (polynomial) spline structures supporting local refinement still retaining the local tensor-product structure. Since Tchebycheffian B-splines are plug-to-plug compatible with polynomial B-splines\, they are naturally compatible with the local refinement structures known for polynomial B-splines (Hierarchical mesh\, T-spline mesh and Locally Refined(LR-) mesh).  \nIn this talk\, we discuss the use of Tchebycheffian splines equipped with a local tensor-product structure as a possible tool in isogeometric analysis with adaptive refinement.\n \n\n \n\n \n Shahroz Khan (U.Strathclyde) - Shape-Supervised Dimension Reduction \n \n \n \nShahroz Khan (U.Strathclyde) – Shape-Supervised Dimension Reduction\nIn shape optimisation problems\, subspaces generated with conventional feature extraction-based design space dimension reduction (DSDR) approaches often fail to extract the intrinsic geometric features of the shape that would allow the exploration of diverse but valid candidate solutions. More importantly\, they also lack incorporation of any notion of physics against which shape is optimised. To simultaneously tackle these deficiencies\, the proposed shape-supervised DSDR uses higher-level information about the shape in terms of its geometric integral properties\, such as geometric moments and their invariants. Their usage is based on the fact that moments of a shape are intrinsic features of its geometry\, providing a unifying medium between geometry and physics. To enrich the subspace with latent features associated with the shape’s geometrical features and physics\, we also evaluate a set of composite geometric moments\, using the divergence theorem\, for appropriate shape decomposition. These moments are combined with the shape modification function to form a decomposed SSV uniquely representing a shape. Afterwards\, the generalised Karhunen–Loève expansion is applied to SSV\, embedded in a generalised (disjoint) Hilbert space\, which results in a basis of the shape-supervised subspace retaining the highest geometric and physical variance.\n \n\n \n\n \n Alexandros Georgiou (USI) - Diffusion models for graph-structured data \n \n \n \nAlexandros Georgiou (USI) – Diffusion models for graph-structured data\nDiffusion (probabilistic) models form a family of generative models that have found great success in various tasks and\, most notably\, image synthesis. We will discuss recent results of diffusion models on graph-structured data and some ideas on how these can be improved.\n \n\n \n \n\nTransportation\n How to get to the venue (“Fresh Hotel”) \nThe venue is located at the center of Athens. The suggested way to reach the location from the Athens International Airport is by using Metro Line 3 (Blue Line) (see map). \nThe metro departs from the airport every 30′ minutes\, and the trip from the Airport to Athens City Center takes around 40 minutes. Information about tickets can be found here. \nYou would need to get off to Monastiraki station. From there following Athinas str. towards Omonia square\, you’d need to make a left on the second traffic lights you’d meet. The hotel is on your right hand side on the 1st corner. This is about 10 minutes walk (700m). \nAlternatively\, from the airport take Metro Line 3 (blue line) or Express Airport Bus X95 or a taxi and at Syntagma Metro Station change to Metro Line 2 (red line). Get off at “Omonia” station. From there it is a 6′ walk (500m) to the venue. \nSchedule of the metro and tram can be found here and of the buses here. \nDirections from/To the Athens International Airport “E. Venizelos” \nThe Athens International Airport is located 27 km southeastern of Athens. \n Directions to Athens center: \n\nThe best option is to use Metro Line 3 (subway\, blue line)\, and get off at “Syntagma” stop. Timetable: 05:30-24:00. Frequency: 36′\nTaxis: the taxi stand extends from Door 4 to Door 1 at the Arrivals Level. Fee: 38 or 54 Euro at daytime or evenings\, resp.\nExpress Airport Bus X95 (to Syntagma sq.): Timetable. Runs on a 24-hour basis. The duration of the route from and to the Eleftherios Venizelos Airport depends on traffic conditions and on the time of use of the Express bus line. Εstimated duration of route for X95 bus line is approximately 60’.\n\n\n Lodging\nWe have reserved a few rooms at the Fresh hotel where the workshop will take place. To book a room please contact the hotel directly and mention that you will be attending the GRAPES workshop. Here are some other suggestions for lodging in Athens in the vicinity of the workshop’s venue: \n\nInn Hotel \nThe Athens Gate Hotel \, Map\nHerodion Hotel\, Map \nPhilippos Hotel\, Map \nAthens Plaza\, Map\nAmalia Hotel Athens\, Map\nElectra Athens\, Map\nElectra Metropolis Athens\, Map\nNew Hotel\nHotel Fresh\, Map URL:http://grapes-network.eu/event/software-industrial-workshop-ii-and-esr-days/ LOCATION:Fresh hotel\, 26 Sofokleous Str.\, Athens\, Attica\, 10552 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220613 DTEND;VALUE=DATE:20220618 DTSTAMP:20260613T034856 CREATED:20220320T112144Z LAST-MODIFIED:20220623T092852Z UID:1761-1655078400-1655510399@grapes-network.eu SUMMARY:Doctoral school II DESCRIPTION:The lake Lugano\n \n \n \n \n \n View on Lugano from Monte San Salvatore\n \n \n \n \n \n Università della Svizzera italiana\, Campus Est\n \n \n\nThe second Doctoral School will take place *physically* from Monday\, June 13 to Friday\, June 17 at the Università della Svizzera italiana (USI) in Lugano. It will start with lunch on Monday and end after lunch on Friday. \nThe event will focus on *CAD* in its broadest sense\, covering the historical development of the field\, latest research results\, applications\, and the relevance for industry. It features keynote talks by GRAPES members and invited high-profile speakers\, short presentation by the ESRs\, work package meetings\, supervisory board and educational committee meetings\, and social activities. \nConfirmed speakers include Fehmi Cirak (U. Cambridge)\, Adi Levin (Meta)\, and Ulrich Reif (TU Darmstadt). \nYou can download a pdf version of the programme here and the book of abstracts here. \nRegistration\nTo register for the workshop\, just send an e-mail with your name\, affiliation\, arrival and departure date\, and dietary restrictions to kai.hormann@usi.ch before *May 20\, 2022*. \nLocation\nThe meeting will take place at the Faculty of Informatics of the Università della Svizzera italiana (USI). \nEast Campus\nVia la Santa 1\n6962 Lugano-Viganello \nThe main event will happen at room D1.14\, on the first floor (one above the ground floor) of the D sector of the East Campus building. The registration desk will be on the ground floor of the D sector. \nTravel instructions\nTo travel to Lugano\, you will fly into Zurich or Milano Malpensa airport and then take a train (approximately 2 hours) to Lugano. Take bus #6 (direction Cornaredo) to get from the train station to the university (get off at stop “Campus Universitario”). \nAccommodation\nLugano has numerous hotels\, but we recommend the following hotels: \n– Hotel City ***\, in walking distance from the university\n– Hotel Pestalozzi **\, near the city centre\n– Hotel Zurigo ***\, near the city centre \nSocial Dinner\nThe social dinner will take place on Thursday evening. Details will be announced here. \nSchedule\nTimes are in Central European Time (CET) zone. (Pdf version) \n  \n\n\n\n\nMonday\, June 13\nTuesday\, June 14\nWednesday\, June 15\nThursday\, June 16\nFriday\, June 17\n\n\n\n\n9:30 – 10:30\n\nKai Hormann (USI) – Novel Range Functions via Taylor Expansions and Recursive Lagrange Interpolation with Application to Real Root Isolation\nTor Dokken (SINTEF) – From CAD for Subtractive Manufacturing to Digital Twins for Additive Manufacturing\nUlrich Reif (TU Darmstadt) – Degenerate Surface Patches\nHendrik Speleers (U. Tor Vergata) – Hierarchical spline spaces and efficient quasi-interpolation\n\n\n10:30 – 11:00\nCoffee Break\n\n\n11:00 – 12:30\nShahroz Khan (ESR 10)\, Krunal Raval (ESR 13)\, Michelangelo Marsala (ESR 6)\nAmrutha B. Nair (ESR 3)\, Athanasios Zoumpekas (ESR 4)\, Rao Fu (ESR 15)\nPablo González Mazón (ESR 5)\, Jean Michel Menjanahary (ESR 14)\, Andriamahenina Ramanantoanina (ESR 12)\nArturs Berzins (ESR 9)\, Alexandros Georgiou (ESR 11)\, Konstantinos Tertikas (ESR 2)\n\n\n12:30 – 14:00\nLunch break\n\n\n14:00 – 15:00\nPanagiotis Kaklis (U. Strathclyde) – Shape-supervised Dimension Reduction\nSam Whyman (ITI) – The Current Status of Subdivision Surface Modelling\nFehmi Cirak (U. Cambridge) – Basis Constructions for Isogeometric Analysis on Unstructured Meshes\nAdi Levin (Meta) – A Ph.D’s career in the tech industry \n\n\n\n15:00 – 15:30\nCoffee Break\nexcursion\n\n\n15:30 – 16:30\nparallel WP meetings\nCarles Checa (ESR 1)\, Eriola Hoxhaj (ESR 7)\nparallel WP meetings\n\n\n16:30 – 17:00\nvisit to CSCS\n\n\n17:00 – 18:30\nEducational Committee meeting\nSupervisory Board meeting [ please login to view/download the slides]\n\n\n18:30 – 19:30\nApéro\n\n\n19:30 – 21:00\n\n\n\nDinner\n\n\n\n  URL:http://grapes-network.eu/event/doctoral-school-ii/ LOCATION:USI\, East Campus\, Via la Santa 1\, Lugano-Viganello\, 6962\, Switzerland ORGANIZER;CN="Kai Hormann":MAILTO:kai.hormann@usi.ch END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20211206 DTEND;VALUE=DATE:20211211 DTSTAMP:20260613T034856 CREATED:20210315T173723Z LAST-MODIFIED:20211208T165137Z UID:1474-1638748800-1639180799@grapes-network.eu SUMMARY:Software and Industrial Workshop I DESCRIPTION:Bord de mer à Antibes\, France – photo Gilbert Bochenek\n \n \n \n \n \n Sophia Antipolis\n \n \n \n \n \n © Inria / photo G. Scagnelli\n \n \n\nThe first Software and Industrial Workshop will offer intersectoral training in technological aspects with links to the industry. It will include lectures and demo sessions so that the ESRs can acquire hands-on experience with software developed within the Network on computational geometry and geometry processing (CGAL)\, isogeometric analysis (G+Smo) and machine learning (PyTorch). Industrial partners will also be invited to give talks and discuss some of their open problems. \nThe meeting will take place physically at the Inria research center at Sophia Antipolis on December 6-10\, 2021. The last two days consist of the annual G+Smo Developer Days workshop.\nAddress : 2004 Route des Lucioles\, 06902 Valbonne\, France \nSocial Dinner\n\nThe social dinner will take place on Wednesday evening in Antibes\, at 19:30\,  at restaurant Alcyon (13 Av. du 11 Novembre). See also this link.\nFor people going back to Nice note that the last train to Nice is at 22:00. \nSchedule\nThe agenda of the workshop includes: Tutorials on Software\, Industrial presentations\, Contributed presentations\, and ESR presentations. \nTimes are in Central European Time (CET) zone. \n\n\n\nTime\nMonday\nTuesday\nWednesday\nThursday\nFriday\n\n\n\n\n9:30 – 10:30\n\nM. Fey (TU Dortmund)- Graph Neural Networks within PyTorch Geometric: Applications\, Implementation and Scalability [online\, Part I]\nCGAL – Tutorial II\nG+Smo Tutorial I:\nIntroduction and basic concepts\nG+Smo: Contributed talks Part I:\nA. Farahat (RICAM)\, P. Weinmüller (JKU)\, S. Imperatore (U. Florence)\n\n\n10:30 – 11:00\n\nBreak\nBreak\nBreak\nBreak\n\n\n11:00 – 12:00\n\nM. Fey (TU Dortmund)- Graph Neural Networks within PyTorch Geometric: Applications\, Implementation and Scalability[online\, Part II]\nCGAL Hands-on session (until  12:30)\nG+Smo Tutorial II: Isogeometric analysis processes\nG+Smo: Contributed talks Part II:\nM. Möller (TU Delft)\, F. Weber (RWTH Aachen)\, H. Verhelst (TU Delft)\n\n\n12:00 – 14:00\nWelcome (13:50)\nLunch break\nLunch break\nLunch break\nLunch break\n\n\n14:00 – 15:00\nD. Mokris (MTU) – Geometric modelling in the CAE pipeline of MTU Aero Engines AG\nCGAL – Tutorial I\nJ. Feydy (Inria Paris)- Fast geometric learning with symbolic matrices – Part I\nG+Smo Tutorial III:\nModules and plugins\nG+Smo Developers’ round-table\n\n\n15:00 – 15:30\nBreak\nBreak\nBreak\nBreak\n\n\n\n15:30 – 17:00\nM. Gammon (ITI) – B-rep\, Subdivision and Facet – Towards a Hybrid Geometry Engine\nCGAL Hands-on session\nJ. Feydy (Inria Paris) – Fast geometric learning with symbolic matrices – Part II\nG+Smo Hands-on session\n\n\n\n17:15\n\nSupervisory Board meeting \nEducational Committee meeting\n\n\n\n\n\nInvited talks and Tutorials\n\nDominik Mokris (MTU) – Geometric modelling in the CAE pipeline of MTU Aero Engines AG\nTogether\, we will have a look at how geometry is handled in the CAE pipeline at MTU. I will show you what our geometry generator does\, its scope\, inputs and outputs as well as why and how it has become indispensable for the design phase of aircraft engines. After discussing some architectural decisions and implementation specifics you will hopefully be better equipped to see whether developing such a tool in your organisation would make sense or not. In the second half\, we will turn our attention to the problem of using 3D scans of physical parts as inputs into the process. Based on the examples of some of the recent [1\, 2] and less recent [3] scientific results you will get an idea of how we work and how long does it take to integrate a scientific idea into day-to-day engineering practice. Joint work with David Großmann (MTU Aero Engines AG\, Germany). \nReferences\n[1] Lisa Groiss\, Bert Jüttler and Dominik Mokriš. 27 variants of Tutte’s theorem\nfor plane near-triangulations and an application to periodic spline surface\nfitting. Comput. Aided Geom. Design\, 85\, 101975\, 2021.\n[2] Cesare Bracco\, Carlotta Giannelli\, David Großmann\, Sofia Imperatore\, Do-\nminik Mokriš and Alessandra Sestini. THB-spline approximations for turbine blade design with local B-spline approximations. Accepted to SEMA-SIMAI granted to MACMAS 2019. Springer.\n[3] Gábor Kiss\, Carlotta Giannelli\, Urška Zore\, Bert Jüttler\, David Großmann\nand Johannes Barner. Adaptive CAD model (re-) construction with THB-\nsplines. Graphical models\, 76(5):273–288\, 2014. \nMark Gammon (ITI) – B-rep\, Subdivision and Facet – Towards a Hybrid Geometry Engine\nThe 3D digital geometry landscape has changed rapidly in recent years with novel forms of geometry reaching industrial maturity. High fidelity 3D scans are now commonplace. Super-flexible subdivision surface geometry powers intuitive free-form design tools. Lattice-based implicit surface geometry driven by simulation algorithms create designs unimaginable with traditional MCAD tools. This rapid expansion of the industrial geometry frontier is exciting and promising\, but geometry exchange weaknesses are limiting opportunities. In this talk I will describe progress ITI is making towards developing a multi-representational geometry engine designed to support alternative\, but concurrent\, geometrical forms of the same master design. An overview of the complex transformations and intelligent associativity between the various representations will be described. Some industrial examples of moving a 3D design between different forms to enable the most optimal representation to be used for a particular operation will be shown. \nMatthias Fey (TU Dortmund) – Graph Neural Networks within PyTorch Geometric: Applications\, Implementation and Scalability\nGraph Neural Networks (GNNs) recently emerged to a powerful approach for representation learning on relational data such as social networks\, molecular graphs or geometry. Similar to the concepts of convolutional and pooling layers on regular domains\, GNNs are able to (hierarchically) extract localized embeddings by passing\, transforming\, and aggregating information between nodes. In this lecture\, I will provide a broad overview of this highly active research field\, and will cover relevant topics such as scalability and applications based on GNNs. In a hands-on session\, you will learn to implement and train GNNs from scratch using the PyTorch Geometric library [https://github.com/pyg-team/pytorch_geometric]. You will learn how to apply GNNs to your own problems and how PyTorch Geometric enables high GPU throughput on highly sparse and irregular data of varying size. \nPyG was started under the name Pytorch Geometric by Matthias Fey and Jan Eric Lenssen during their PhDs at TU Dortmund University. After its release in November 2017\, the state-of-the-art in Graph Neural Networks has been quickly growing in leaps and bounds over the past few years\, including significant contributions from Stanford University such as GraphSAGE\, the Open Graph Benchmark and GraphGym. To support the continuous appetite of new features and to ensure longevity\, TU Dortmund and Stanford University have joined forces and are now committed to the maintenance and enhancement of the package in accordance with the latest trends in academic research. GNN tools from both parties are unified under the umbrella PyG\, which now involves a team of core developers extended by a community of over 170 contributors across the world. \nPierre Alliez (Inria)\, Andreas Fabri (GeometryFactory) – CGAL Tutorial\nIn this tutorial on CGAL library (https://www.cgal.org/) we will present various CGAL packages from the user perspective\, that is we will not present the theory behind the algorithms and data structures. By “CGAL packages”\, we mean the different chapters in the online manual as you can find them in the CGAL Package Overview page: https://doc.cgal.org/latest/Manual/packages.html. The goal is to be able to write code during the workshop where you will use CGAL\, whether you have never used CGAL before or are an advanced user. \nJean Feydy (Inria Paris) – Fast geometric learning with symbolic matrices\nSparse representations such as 3D point clouds have a key position in the computer vision toolbox. They complement bitmap images effectively\, enabling fast geometric computations for e.g. shape registration. In this talk\, I will present extensions for PyTorch\, NumPy\, Matlab and R that speed up fundamental computations on (generalized) point clouds by several orders of magnitude\, compared with PyTorch\, TF and JAX GPU baselines. These software tools allow researchers to break through major computational bottlenecks in the field and have been downloaded more than 100k times over the last few years. The presentation will be of interest to all researchers who deal with point clouds\, time series and segmentation maps\, with a special focus on: \n1. Fast and scalable computations with (generalized) distance matrices.\n2. Efficient and robust solvers for the optimal transport (= “Earth Mover’s”) problem.\n3. Applications to shape analysis and geometric deep learning\, with a case study on the “pixel-perfect” registration of lung vessel trees. \nThe tutorial will be a hands-on introduction to fast geometric computations with the KeOps library. We will use Google Colab as a simple GPU sandbox. \nReferences:\n– “Geometric data analysis\, beyond convolutions”: https://www.jeanfeydy.com/geometric_data_analysis.pdf\n– “Fast geometric learning with symbolic matrices”: http://jeanfeydy.com/Papers/KeOps_NeurIPS_2020.pdf\n– “Accurate point cloud registration with robust optimal transport”: https://www.jeanfeydy.com/Papers/RobOT_NeurIPS_2021.pdf\n– KeOps library (geometric computations): http://kernel-operations.io/keops/index.html\n– GeomLoss library (optimal transport): https://www.kernel-operations.io/geomloss/ \nAngelos Mantzaflaris (Inria)\, Hugo Verhelst (TU Delft) – G+Smo Tutorial\nThe tutorials on Thursday will give an overview of the open-source library “G+Smo” and its capabilities\, as well as hands-on session in the afternoon. G+Smo is a C++ library that brings together mathematical tools for geometric design and numerical simulation. It implements the relatively new paradigm of isogeometric analysis\, which suggests the use of a unified framework in the design and analysis pipeline. The library aims at providing access to high quality\, open-source software to the community of numerical simulation and beyond. \nContributed talks for G+Smo Developer Days\n\nAndrea Farahat (RICAM\, Austria)\nIsogeometric analysis with C1-smooth functions over multi-patch surfaces.\nA framework for the construction of a C1-smooth isogeometric spline spaces over particular G1 multi-patch surfaces\, called analysis-suitable G1 (AS-G1) surfaces \, will be presented. The class of AS-G1 multi-patch geometries is of importance since it includes exactly those G1-smooth multi-patch geometries which allow the design of C1-smooth isogeometric spline spaces with optimal approximation properties. The method extends the construction for AS-G1 planar multi-patch parametrizations to the AS-G1 multi-patch surface case. We also generate for the C1-smooth isogeometric spline space a local basis which is used to solve\, by a standard Galerkin approach\, the biharmonic equation — a particular fourth order partial differential equation — over several AS-G1 multi-patch surfaces. The obtained numerical results exhibit optimal convergence orders in the L2\, H1 and H2 norm\, and demonstrate the potential of our C1-smooth isogeometric spline functions for solving fourth order partial differential equations over multi-patch surfaces. \nPascal Weinmüller (JKU Linz) – Solving fourth order equations on multpatches with isogeometric analysis in G+Smo\nIsogeometric analysis (IGA) is a numerical method that uses spline-based geometry parameterizations to solve partial differential equations (PDEs). Since IGA is based on B-splines\, it is simple to achieve high order smoothness within a single patch. However\, to represent more complex geometries one usually uses a multi-patch construction. In this case\, the global continuity for the basis functions is in general only C^0. Therefore\, for C^1-smooth isogeometric functions\, a special construction for the basis is needed. Such spaces are of interest when solving numerically fourth-order PDE problems\, such as the biharmonic equation or Kirchhoff-Love plate/shell formulations\, using an isogeometric Galerkin method.\nThere are several different methods to overcome the problem of reduced smoothness. One way is to use isogeometric spaces that are globally C^1 over multi-patch domains where different constructions are studied in [1\,2\,3].\nHowever\, each approaches need a different construction\, but end with the same problem: solving the PDEs. In this talk\, a method in G+Smo is shown which handles the different spaces by using the class “gsMSpline” for constructing the spaces. The main idea is that it uses basis transformation for describing the space which makes it in general simple for any bases. The handling of the class is kept straightforward and we show an example with different basis constructions which can be used for solving the biharmonic equation on multi-patch domains.\nJoint work with Hugo Verhelst\, TU Delft\, Netherlands\, Andrea Farahat\, RICAM Linz\, Austria and Angelos Mantzaflaris\, Inria\, France\n[1] Hughes\, T. JR and Sangalli\, G. and Takacs\, T. and Toshniwal\, D.\, Smooth multi-patch discretizations in Isogeometric Analysis\, Handbook of Numerical Analysis\, (2021) Vol 22: 467–543.\n[2] Kapl\, M.\, Sangalli\, G.\, Takacs\, T. An isogeometric C^1 subspace on unstructured multi-patch planar domains. Computer Aided Geometric Design\, (2019) Vol 69: 55–75.\n[3] Weinmüller\, P. and Takacs T.\, Construction of approximate C^1 bases for isogeometric analysis on two-patch domains\, Computer Methods in Applied Mechanics and Engineering\, (2021) Vol 385: 114017. \nSofia Imperatore (U. Florence) – Data-driven spline parametrization and fitting problems\nFree-form curve fitting plays an important role in Computer Aided Geometric Design (CAGD) and Geometric Modelling. In particular\, in many applications geometric models are built on measured data and subsequently employed for further design. A necessary pre-processing step to fitting a given sequence of points with spline curves is to first compute suitable parameterizations. The talk will investigate different neural network architectures to address this problem for different kinds of point sequences. \nMatthias Möller (TU Delft) – Physics-Informed Machine Learning Embedded Into Isogeometric Analysis\nPhysics-informed neural networks (PINNs) are emerging technologies that strive to revolutionize the field of computer-aided analysis of scientific and engineering problems by directly exploiting physical laws to drive network optimization. In this short talk we propose a novel approach to embed the PINN paradigm into the framework of Isogeometric Analysis. In contrast to classical PINNs which predict point-wise solution values to (initial-)boundary-value problems directly\, our IGA-PINNs learn solutions in terms of their expansion coefficients relative to a given B-Spline basis. This approach is furthermore used to encode the geometry and other problem parameters such as boundary conditions and feed them into the network as inputs\, which allows the user to analyze different problem configurations effectively. \nFelix Weber (RWTH Aachen) –  Isogeometric Analysis for Micromechanical Analysis of Cast Iron\nMaterial properties such as fatigue strength highly depend on the local microstructure. With increasing complexity of microstructure models finite element based approaches become more costly. Therefore\, IGA-suitable 2D-modelling of microstructures becomes relevant and allows for a decrease in required degrees of freedom making further analysis feasible. \nHugo Verhelst (TU Delft) – A Parallel Adaptive Arc-Length Method\nParallel computing is omnipresent in today’s scientific computing landscape starting at multicore processors in desktop computers up to massively parallel clusters. While domain decomposition methods have a long tradition in computational mechanics to decompose spatial problem into many subproblems that can be solved in parallel\, advancing solution schemes for dynamics or quasi-statics are inherently serial processes. Methods like Parareal and Multigrid-reduction-in-time (MGRIT) are based on a multigrid approach over the temporal domain\, enabling parallelization in time. These techniques have been applied successfully in many fields of study\, including power network analysis [1]\, cardiac fluid-structure interaction [2] or the training of neural networks [3].\nAs MGRIT is based on dynamic\, their temporal multigrid scheme is based on a time discretization error that evolves over time. For quasi-static simulations\, solutions at different time steps are independent so that the time discretization error and thus the motivation for MGRIT vanishes.\nIn this talk\, we will present an MGRIT-inspired approach to parallelize quasi-static computations. Due to the parametrization of the arc-length instead of time\, the multi-level error for the arc-length parametrization is formed by the load parameter and the solution norm; enabling to measure errors like MGRIT. By applying local refinements in the arc-length parameter\, this multi-level adaptive arc-length method refines solutions where the non-linearity in the load-response space is maximal\, i.e. where the path is curved. To reduce the number of levels needed for reference errors\, a higher-dimensional spline is adaptively fitted through the load-response solution space. Furthermore\, knot insertion algorithms will assure that bifurcations can nicely be embedded in the presented method.\nReferences\n[1] S. Günther\, R. D. Falgout\, P. Top\, C. S. Woodward and J. B. Schroder\, “Parallel-in-Time Solution of Power Systems with Unscheduled Events\,” 2020 IEEE Power & Energy Society General Meeting (PESGM)\, 2020\, pp. 1-5\, doi: 10.1109/PESGM41954.2020.9281595.\n[2] Hessenthaler\, A.\, Falgout\, R. D.\, Schroder\, J. B.\, de Vecchi\, A.\, Nordsletten\, D.\, & Röhrle\, O. (2021). Time-periodic steady-state solution of fluid-structure interaction and cardiac flow problems through multigrid-reduction-in-time. arXiv preprint arXiv:2105.00305.\n[3] Cyr\, E. C.\, Günther\, S.\, & Schroder\, J. B. (2019). Multilevel initialization for layer-parallel deep neural network training. arXiv preprint arXiv:1912.08974. \nLocation\nThe meeting will take place at the Inria research center at Sophia Antipolis: Inria Sophia Antipolis Méditerranée 2004\, route des Lucioles BP 93 06902 Sophia Antipolis Cedex. You can find travel directions to Inria here. \nLodging\nHere are some suggestions for lodging in Antibes with an easy access by bus to Sophia Antipolis by bus. \n\nHôtel Le Collier\, http://www.lecollier-hotelrestaurant.com/\n3 minutes by walk from the bus stop “pôle d’échange Antibes” (gare SNCF)\, bus line A to Inria.\nHôtel de l’Etoile\, http://www.hoteletoile.com/\n7 minutes by walk from the bus stop “pôle d’échange Antibes (gare SNCF)\, bus line A to Inria.\nHôtel Josse\, http://www.hotel-josse.com/ (more expensive)\n20 minutes by walk from the bus stop “pôle d’échange Antibes (gare SNCF)\, bus line A to Inria.\nRelais du Postillon\, http://www.relaisdupostillon.com/\n8 minutes by walk from the restaurant where the social event will take place.\nIbis Style Antibes\, https://www.booking.com/hotel/fr/ibis-styles-antibes.html\nOn the way from Antibes to Inria (bus line A)\, but not downtown.\n\nSchedule of the buses can be found here :\nhttps://www.envibus.fr/en/bus-routes/ligne-a.html \nMore Hotels in Sophia Antipolis (from Nice Airport\, you can take bus 230 to go to Sophia Antipolis)\n \n\nMoxy Sophia Antipolis – 400m from Inria – from 120€/night\nHôtel Novotel Antibes Sophia Antipolis – 900m from Inria – From 100€/night\nNemea Appart’Hotel Biot Sophia Antipolis Green Side – 1km from Inria – From 60€/night\nHotel Omega Valbonne – 1\,2km from Inria – From 100€/night\nHôtel ibis Antibes Sophia Antipolis – 1\,4km from Inria – From 90€/night\nHôtel Mercure Antibes Sophia Antipolis – 1\,4km from Inria – From 102€/night\nMouratoglou Resort – 1\,5km from Inria – From 140€/night\nB&B HOTEL – 1\,5km from Inria – From 60€/night\n\n More Hotels in Antibes (10km from Inria – can reach Sophia Antipolis by bus A and bus 9) \n (from Nice Airport\, you can go to Nice Saint-Augustin train station by walking or taking a tram (see more) then take train TER from Nice Saint-Augustin to Antibes) \n\nHôtel Le Collier (in front of Antibes train station – convenient for bus to Sophia Antipolis) – From 70€/night\nIrin Hotel (in Antibes old town) – From 80€/night\nHôtel de L’Etoile (550m from Antibes train station and 600m from Place de Gaulle\, main square of Antibes old town) – From 80€/night\nHôtel La Place (in Antibes old town) – From 93€/night\nBest Western Hotel Journel Antibes – From 90€/night\nBest Western Plus Antibes Riviera (1\,7km from Antibes train station) – From 110€/night\nRESIDÉAL Antibes (1\,6km from Antibes train station) – From 300€/week\nRoyal Antibes Luxury Hotel\, Residence\, Beach & Spa – From 115€/night\nHôtel Josse – From 90€/night\n\nTransporation\nTransportation from Nice Airport \n\nTaxi at around 70€ from Nice Airport to Sophia Antipolis\nBus 230 (direction Sophia Gare) from Nice Airport to Sophia Antipolis with stop at IUT-INRIA or SKEMA (if you want to go to Novotel Sophia Antipolis)\nThe bus price of single travel is 1.5 €\nThe schedule of bus 230: https://services-zou.maregionsud.fr/fr/horaires/Zou%2006/BUS/ligne/LR230/direction/OUTWARD/697\n\n Transportation from Antibes train station (TGV/Thalys station): \n\nIf you take the train or TGV\, you can stop at Antibes train station.\nFrom Antibes train station\, you can take the following buses for 1€ to go to Sophia Antipolis\nBus A (direction GR Valbonne Sophia Antipolis) : https://envibus-api.koezio.com/uploads/fiche/ENVIBUS_Ligne%20A_sept2021_web.pdf with stop at INRIA or at SKEMA (if you want to go to Novotel Sophia Antipolis)\nBus 12 (direction GR Valbonne Sophia Antipolis): https://envibus-api.koezio.com/uploads/fiche/ENVIBUS%20Fiche%20Horaires%20Ligne%2012_sept2021_v2_web.pdf with stop at INRIA\nThe bus stop name at Antibes train station is ‘Pôle Echanges’\n\n\n Transportation from Antibes old town: \n\nIf your hotel is in Antibes old town\, you can go to Place de Gaulle to take the bus to Sophia Antipolis\nFrom Antibes old town\, you can take the following buses for 1€ to go to Sophia Antipolis\nBus A (direction GR Valbonne Sophia Antipolis) : https://envibus-api.koezio.com/uploads/fiche/ENVIBUS_Ligne%20A_sept2021_web.pdf with stop at INRIA or at SKEMA (if you want to go to Novotel Sophia Antipolis). The bus stop name in Antibes for bus A is ‘Dugommier’\nBus 9 (GR Valbonne Sophia Antipolis): https://envibus-api.koezio.com/uploads/fiche/ENVIBUS%20Fiche%20Horaires%20Ligne%209_PScolaire_sept2021_web.pdf with stop at ST PHILIPPE then walk to Inria. The bus stop name in Antibes for bus 9 is ‘Place de Gaulle’ URL:http://grapes-network.eu/event/software-and-industrial-workshop-i/ LOCATION:Inria Sophia-Antipolis\, 2004 Route des Lucioles\, Sophia Antipolis \, Valbonne\, 06902\, France ORGANIZER;CN="Lurent Buse":MAILTO:laurent.buse@inria.fr END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20210906 DTEND;VALUE=DATE:20210911 DTSTAMP:20260613T034856 CREATED:20210304T103046Z LAST-MODIFIED:20230719T072621Z UID:1457-1630886400-1631318399@grapes-network.eu SUMMARY:Learning Week I: Academic skills and advanced topics in CAD DESCRIPTION:The first Learning Week will take place in Barcelona from September 6 to 10\, 2021. It will be a physical event but talks will be broadcasted online via zoom for those who cannot attend in person. Updated information on the COVID19 related measures in force in Barcelona can be found here. \nThe event will be co-organised with the Barcelona Graduate School of Mathematics (BGSMath) and the University of Strathclyde. It aims to help ESRs develop complementary and transferable skills in Academia. Topics to be covered include communication/presentation to diverse audiences\, Fund-raising and Proposal writing\, and Knowledge Transfer Basics. These sessions will often include hands-on activities. \nThe event will include a 2-day scientific workshop with courses on advanced topics of CAD. Confirmed speakers include Sandra Di Rocco (KTH)\, Denys Plakhotnik (ModuleWorks)\, Christian Arber (Topsolid)\, Michael Floater (U. Oslo)\, Tamás Várady (Budapest U. of Technology and Economics) and Jiri Kosinka (U. Groningen). \nYou can download a pdf version of the preliminary programe here. \nRegistration\nOnline registration is now closed! \nLocation\nThe meeting will take place at the Institut de Matematiques of the University of Barcelona. \nGran Via de les Corts Catalanes\, 585\n08007 Barcelona \nAll talks and meetings will happen at Room T1\, on the top (second) floor of the Historic Building of the University of Barcelona\, on the premises of the Mathematics Faculty (http://www.mat.ub.edu/). \nHow to Arrive \nThe Mathematics Faculty is located next to Plaça de la Universitat (University Square). The subway lines L1 (red) and L2 (purple) have stations at Plaça Universitat and many city buses pass through this area. See metro and bus networks (http://www.tmb.cat/en/home). \nThe Historic Building is at a short walking distance from Plaça de Catalunya\, the city’s nerve center. \nSchedule of the Learning Week (last updated 4/09/2021)\npdf version \nThe event will be broadcasted online via zoom. It will be accessible via this link. \n\nMonday\, September 6th\, 2021\n14:00 Brief introduction of the fellows\n16:00 Coffee break\n16:30 Marc Noy (Polytechnic University of Catalonia): How to talk and write mathematics\, a personal perspective\n18:00 End of the day\n \n\nTuesday\, September 7th\, 2021\n09:00 Albert Ruiz (Autonomous University of Barcelona) — Advanced Latex [slides]\n10:30 Coffee break\n11:00 Núria Fagella (University of Barcelona) — Do’s and Don’ts about giving math talks [slides]\n13:00 Lunch break\n14:30 Jordi Mullor (Centre de Recerca Matematica) — Tips and basics for remote presentations [slides]\n16:00 Coffee break\n16:30 Conversations with Marta Canadell\, PhD in Mathematics and currently working in the Tech Industry\n18:30 Welcome reception\n \n\nFriday\, September 10th\, 2021\n09:00 Oral Expositions\n10:30 Coffee break\n11:00 Oral Expositions\n13:00 End of the Learning Week\n \nSchedule of the Workhsop on Advanced Topics in CAD\npdf version \n\nWednesday\, September 8th\, 2021\n09:00 Christian Arber (TopSolid): GCS Geometric Constraint System … and CAD Solvers: some Maths behind that [slides]\n10:30 Coffee break\n11:00 Denys Plakhotnik (ModuleWorks): Geometric problems and software implementation in Computer Aided Manufacturing (CAM) \n13:00 Lunch break\n14:30 Sandra di Rocco (KTH): Algebraic Geometry of Data\n16:00 End of the day\n \n\nThursday\, September 9th\, 2021\n09:00 Tamas Varady (Budapest University of Technology and Economics): Multi-sided\, multi-connected surface\npatches for Computer Aided Design\n10:30 Coffee break\n11:00 Michael Floater (University of Oslo): Surface parameterization\n13:00 Lunch break\n14:30 Jiri Kosinka (University of Groningen): Subdivision (and other) surfaces in CAD and graphics\n16:00 Coffee break\n16:30 Free time for discussions\n18:00 Reception URL:http://grapes-network.eu/event/learning-week1/ LOCATION:University of Barcelona\, Gran Via de les Corts Catalanes 585\, Barcelona\, 08007\, Spain ORGANIZER;CN="Carlos D'Andrea":MAILTO:cdandrea@ub.edu END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20210201 DTEND;VALUE=DATE:20210209 DTSTAMP:20260613T034856 CREATED:20201021T103429Z LAST-MODIFIED:20210211T180135Z UID:943-1612137600-1612828799@grapes-network.eu SUMMARY:Doctoral school I & Midterm meeting DESCRIPTION:Stoa of Attalos\n \n \n \n \n \n Academy of Athens\n \n \n \n \n \n Acropolis\n \n \n\nDue to the covid19 pandemic\, the first Doctoral School and the Midterm meeting will be held online during the first week of February 2021. Despite its name\, the Midterm meeting with the REA Project Officer is held on February 4\, 2021 as an one day event. Both events will be held on Zoom\, links are emailed to the participants. Please join the meeting using your full name! \nSchedule of the Doctoral School I\nThe First Doctoral School focuses on machine learning for shapes. There will be introductory short courses and presentations by GRAPES members and other invited high-profile speakers. Confirmed speakers include P. Alliez (Inria & GF)\, Y. Avrithis (Inria & Athena RC)\, M. Bronstein (USI and ICL)\, M. Salamó (UB)\, and A. Leutgeb from industrial partner RISC-Software. A pdf version of the schedule is also available. \n\nMonday\, February 1st\, 2021 (Chair: I. Emiris)\n11:00 Tutorial [Part A]: Pierre Alliez (Inria & GF): Clustering algorithms and introduction to persistent homology \nAbstract: This tutorial will first offer an introduction to clustering\, both discrete and continuous\, and persistent homology: K-means\, Lloyd iteration\, hierarchical clustering\, single linkage algorithm\, dendograms\, mode seeking clustering\, persistent homology. I will also provide a basic introduction to machine learning\, neural networks\, and related data structures.\n12:30 Lunch break\n14:00 Tutorial [Part B]: Pierre Alliez (Inria & GF): Machine Learning 101 and Neural networks \n15:30 Coffee break\n17:00 Invited talk:Thierry Chevalier (Airbus): Industrial interest in learning\, processing & optimising shapes \n18:00 End of the day\n \n\nTuesday\, February 2nd\, 2021 (Chair: M. Salamó)\n11:00 Tutorial [Part A]: Yannis Avrithis (Inria & Athena RC): Deep learning and computer vision \nAbstract: This tutorial will comprise of the following parts: (a) Visual representation: Data-driven representation learning. Neuroscience and receptive fields. Visual descriptors. Hierarchical representations. (b) Machine learning: Linear classification\, binary and multi-class. Multiple layers\, neural networks. (c) Convolution: Definition\, convolutional networks. Pooling. Network architectures. (d) Optimization: Gradient computation. Optimizers\, initialization\, normalization. Deeper architectures. (e) Retrieval: Spatial pooling. Metric learning and image retrieval. Unsupervised and semi-supervised learning. Graph-based methods.\n12:30 Lunch break\n14:00 Tutorial [Part B]: Yannis Avrithis (Inria & Athena RC): Deep learning and computer vision \n15:30 Coffee break\n16:00 Supervisory Board meeting (restricted)\n17:00 End of the day\n \n\nWednesday\, February 3rd\, 2021 (Chair: A. Mantzaflaris)\n11:00 Tutorial [Part A]: Michael Bronstein (USI & Imperial College London) \n12:30 Coffee break\n13:00 Tutorial [Part B]: Michael Bronstein (USI & Imperial College London) \n14:30 Lunch break\n16:00 “Social” activities: Virtual tours of the Acropolis: #1\, #2\, and VR 360ᵒ video tour of the Acropolis Museum.\n \n\nThursday\, February 4th\, 2021\nMeeting with the REA Project Officer: schedule and its pdf version.\n \n\nFriday\, February 5th\, 2021 (Chair: G. Muntingh)\n11:00 Tutorial [Part A]: Maria Salamó (UB): Point-cloud analysis and classical Machine Learning techniques \nAbstract: The analysis of point cloud has been done in different ways in the field of machine learning. This tutorial is devoted to introducing the main machine learning algorithms that can be used for point-cloud analysis\, among other applications. It has been divided in two parts. First part is focused on the theoretical aspects of classical machine learning algorithms\, while the second part is devoted to deep learning algorithms. In both parts\, the theoretical foundations of every type of algorithm will be detailed. We will see practical examples of 3D shapes analysis on every part.\n12:30 Lunch break\n14:00 Tutorial [Part B]: Simone Balocco (UB): Deep learning approaches \n15:30 Coffee break\n16:00 Educational Committee meeting (restricted)\n16:30 End of the day\n \n\nMonday\, February 8th\, 2021\n11:00 Tutorial [Part A]: Alexander Leutgeb (RISC-Software): Dynamic Cutting Force Prediction (During Simulation of Machining Processes) (Chair: A. Fabri) \nAbstract: The problem domain is to predict the cutting forces during simulation of metal cutting processes. There already exist several force computation models. These models have in common\, that for different material combinations (cutting tool and workpiece) the determination of their coefficients is a time consuming task by performing different metal cutting patterns with the real machine and measuring the resulting cutting forces over the time. The novel approach of our research project should learn this coefficients from regular machining processes. That means we need on the one hand the data from the simulation (machining parameters and geometric representation of cutter workpiece engagement) and on the other hand the measured forces of the sensors from the real machine. Because usually the real machines do not come with sensors providing force measurements we will use an Internet-of-Things sensor from Pro Micron\, which measures the forces directly on the tool side. So from a Machine Learning standpoint we have to first correlate the time series data from the simulation with that of the sensor. Later on we perform model fitting/regression (linear & non linear) to learn the coefficients for the force computation model.\nAfter the model is trained\, the simulation of the machining process can predict the cutting forces over the time. The application of these forces is relevant for machine vibration analysis\, feed rate optimization\, increase of energy efficiency\, geometric errors in finished workpieces caused by tool cutter deflections\, and so on.\n12:00 Lunch break\n14:00 Tutorial [Part B]: Christoph Hofer (RISC-Software): Dynamic Cutting Force Prediction (During Simulation of Machining Processes) (Chair: A. Fabri) \n15:00 Coffee break\n17:00 Seminar: Managing and sharing research outputs: all you need to know — SLIDES & VIDEO\nPresenters: Elli Papadopoulou\, Athena RC/ OpenAIRE\, Iryna Kuchma\, Electronic Information for Libraries – eifl / OpenAIRE\nAbstract: This is an introductory session to the Open Science model. It aims to ensure that all ESRs have a comprehensive understanding of the basics of Open Science and are able to apply them in their everyday work using available resources. Presentations will highlight good practices in scientific publishing and data management activities that increase researchers recognition and at the same time achieve compliance with European requirements (e.g. grants received under H2020\, HorizonEurope and ERC). A “first-aid toolkit” will be provided so that researchers can practice Open Science skills at their own time and pace.\n18:15 End of the Doctoral School\n \n\nSchedule of the Midterm Meeting (February 4th\, 2021)\nThe purpose of the Midterm meeting between the REA Project Officer (Filippo Galiardi) and the Consortium is as to discuss the implementation of the project\, make sure that all ESRs have been recruited and review the rules of the Marie Curie programme to see if there are any issues that need to be addressed before the beginning of the scientific activities. All beneficiaries and ESRs should participate the meeting. Our Partner Organisations are strongly encouraged to participate. \nYou can find a pdf version of the Midterm meeting program here. All times are Greek local time (EET=UTC+2=GMT+2). \n\n10:00 Introduction by the Project Coordinator and the REA Project Officer\n10:05 Tour de table: All scientists-in-charge briefly present their research team and describe their role within the network. Introduction of the Partner Organisations\n10:45 REA Project officer presentation \n11:05 Coordinator’s report: Presentation of the Network and the Progress report \n11:35 Lunch break\n13:00 Fellows’ individual presentations\n14:00 Coffee break\n14:15 Fellows’ individual presentations\n15:00 Restricted Meeting between the fellows and the Project Officer\n16:00 Coffee break\n16:15 Restricted session: Meeting between site-leaders and Project Officer\n16:30 Feedback and open discussion\n17:00 End of the meeting\n \n[slideshow_deploy id=’1377′] URL:http://grapes-network.eu/event/doctoral-school-i-midterm-meeting/ END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Paris:20201105T110000 DTEND;TZID=Europe/Paris:20201106T170000 DTSTAMP:20260613T034856 CREATED:20191214T131217Z LAST-MODIFIED:20210801T063548Z UID:754-1604574000-1604682000@grapes-network.eu SUMMARY:Kickoff & Recruitment Event\, Rome (Virtual Event) DESCRIPTION:GRAPES Kickoff Event\n\n \n \n \n \n Roman Forum\n \n \n \n \n \n Trevi Fountain\n \n \n \n \n \n Pantheon\n \n \n\nDue to the coronavirus pandemic\, our Kickoff Workshop will be held online on November 5-6\, 2020. It will not be a “recruitment” event as it was initially planned because most of the positions are now closed. However\, the application process for the remaining open positions is ongoing and we encourage interested candidates to upload their files (see this page) since the hard deadline to recruit PhD fellows remains end of November 2020.\n \nSchedule\nThe agenda of the meeting includes introductory presentations by the coordinator\, the research coordinator\, the head of the Educational Committee and the technical coordinator. Next\, each beneficiary will give a short presentation of their team and their PhD projects (15′ for teams hosting one PhD\, 25′ for teams hosting two PhDs). The PhD projects will be presented by the ESRs\, if they are recruited by the time of the Kickoff\, else by the project advisor. We encourage the participation of short listed or selected candidates that will not have completed their recruitment procedures by the start of the Kickoff event. The Project’s Partner Organizations will give a brief overview of their research activities and their expectations from GRAPES. Finally\, there will be meetings of the Educational Committee and the Supervisory Board. \nYou can download a pdf version of the program here.\nAll times are Rome local time (UTC+1). \n\n\n\n\nThursday November 5th\, 2020\n\nFriday November 6th\, 2020\n\n\n\n\n11:00\nC. Manni – welcome\n\n11:00\nUSI Lugano \, ESR11\, ESR12 \n\n\n11:05\nI. Emiris – GRAPES Coordinator \n\n11:25\nUniv. Rome Tor Vergata\, ESR13 \n\n\n11:20\nL. Busé – Research Coordinator\n\n11:40\nVilnius Univ. \, ESR14 \n\n\n11:35\nC. D’Andrea – Head of Educational Committee \n\n11:55\nGeometryFactory \, ESR15\n\n\n11:50\nC. Konaxis – Technical Coordinator \n\n12:10\nLunch break\n\n\n12:00\nLunch break\n\n14:00\n3D Industries\n\n\n13:45\nAthena RC \, Y. Avrithis \, ESR1\, ESR2 \n\n14:15\nInternational TechneGroup Ltd \n\n\n14:10\nUniv. Barcelona\, ESR3\, ESR4\n\n14:30\nModuleWorks GmbH \n\n\n14:35\nInria \, ESR5 \, ESR6\n\n14:45\nRISC-Software GmbH \n\n\n15:00\nCoffee break\n\n15:00\nCoffee break\n\n\n15:15\nJKU Linz\, ESR7\n\n15:15\nSupervisory Board meeting \n\n\n15:30\nRWTH Aachen\, ESR8\n\n\n\n\n\n15:45\nSINTEF\, ESR9\n\n\n\n\n\n16:00\nUniv. Strathclyde – ESR10 \n\n\n\n\n\n16:15\nCoffee break\n\n\n\n\n\n16:30\nEducational Committee meeting URL:http://grapes-network.eu/event/kickoff-recruiting-event-rome/ END:VEVENT END:VCALENDAR