Software and Industrial Workshop I

The 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.

The 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.
Address : 2004 Route des Lucioles, 06902 Valbonne, France

Social Dinner

The 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.
For people going back to Nice note that the last train to Nice is at 22:00.

Schedule

The agenda of the workshop includes: Tutorials on Software, Industrial presentations, Contributed presentations, and ESR presentations.

Times are in Central European Time (CET) zone.

Time Monday Tuesday Wednesday Thursday Friday
9:30 – 10:30 M. Fey (TU Dortmund)- Graph Neural Networks within PyTorch Geometric: Applications, Implementation and Scalability [online, Part I] CGAL – Tutorial II G+Smo Tutorial I:
Introduction and basic concepts
G+Smo: Contributed talks Part I:
A. Farahat (RICAM), P. Weinmüller (JKU), S. Imperatore (U. Florence)
10:30 – 11:00 Break Break Break Break
11:00 – 12:00 M. Fey (TU Dortmund)- Graph Neural Networks within PyTorch Geometric: Applications, Implementation and Scalability[online, Part II] CGAL Hands-on session (until  12:30) G+Smo Tutorial II: Isogeometric analysis processes G+Smo: Contributed talks Part II:
M. Möller (TU Delft), F. Weber (RWTH Aachen), H. Verhelst (TU Delft)
12:00 – 14:00 Welcome (13:50) Lunch break Lunch break Lunch break Lunch break
14:00 – 15:00 D. Mokris (MTU) – Geometric modelling in the CAE pipeline of MTU Aero Engines AG CGAL – Tutorial I J. Feydy (Inria Paris)- Fast geometric learning with symbolic matrices – Part I G+Smo Tutorial III:
Modules and plugins
G+Smo Developers’ round-table
15:00 – 15:30 Break Break Break Break
15:30 – 17:00 M. Gammon (ITI) – B-rep, Subdivision and Facet – Towards a Hybrid Geometry Engine CGAL Hands-on session J. Feydy (Inria Paris) – Fast geometric learning with symbolic matrices – Part II G+Smo Hands-on session
17:15 Supervisory Board meeting Educational Committee meeting

Invited talks and Tutorials

Dominik Mokris (MTU) – Geometric modelling in the CAE pipeline of MTU Aero Engines AG

Together, 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).

References
[1] Lisa Groiss, Bert Jüttler and Dominik Mokriš. 27 variants of Tutte’s theorem
for plane near-triangulations and an application to periodic spline surface
fitting. Comput. Aided Geom. Design, 85, 101975, 2021.
[2] Cesare Bracco, Carlotta Giannelli, David Großmann, Sofia Imperatore, Do-
minik 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.
[3] Gábor Kiss, Carlotta Giannelli, Urška Zore, Bert Jüttler, David Großmann
and Johannes Barner. Adaptive CAD model (re-) construction with THB-
splines. Graphical models, 76(5):273–288, 2014.

Mark Gammon (ITI) – B-rep, Subdivision and Facet – Towards a Hybrid Geometry Engine

The 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.

Matthias Fey (TU Dortmund) – Graph Neural Networks within PyTorch Geometric: Applications, Implementation and Scalability

Graph 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.

PyG 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.

Pierre Alliez (Inria), Andreas Fabri (GeometryFactory) – CGAL Tutorial

In 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.

Jean Feydy (Inria Paris) – Fast geometric learning with symbolic matrices

Sparse 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:

1. Fast and scalable computations with (generalized) distance matrices.
2. Efficient and robust solvers for the optimal transport (= “Earth Mover’s”) problem.
3. Applications to shape analysis and geometric deep learning, with a case study on the “pixel-perfect” registration of lung vessel trees.

The 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.

References:
– “Geometric data analysis, beyond convolutions”: https://www.jeanfeydy.com/geometric_data_analysis.pdf
– “Fast geometric learning with symbolic matrices”: http://jeanfeydy.com/Papers/KeOps_NeurIPS_2020.pdf
– “Accurate point cloud registration with robust optimal transport”: https://www.jeanfeydy.com/Papers/RobOT_NeurIPS_2021.pdf
– KeOps library (geometric computations): http://kernel-operations.io/keops/index.html
– GeomLoss library (optimal transport): https://www.kernel-operations.io/geomloss/

Angelos Mantzaflaris (Inria), Hugo Verhelst (TU Delft) – G+Smo Tutorial

The 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.

Contributed talks for G+Smo Developer Days

Andrea Farahat (RICAM, Austria)

Isogeometric analysis with C1-smooth functions over multi-patch surfaces.
A 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.

Pascal Weinmüller (JKU Linz) – Solving fourth order equations on multpatches with isogeometric analysis in G+Smo

Isogeometric 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.
There 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].
However, 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.
Joint work with Hugo Verhelst, TU Delft, Netherlands, Andrea Farahat, RICAM Linz, Austria and Angelos Mantzaflaris, Inria, France
[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.
[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.
[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.

Sofia Imperatore (U. Florence) – Data-driven spline parametrization and fitting problems

Free-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.

Matthias Möller (TU Delft) – Physics-Informed Machine Learning Embedded Into Isogeometric Analysis

Physics-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.

Felix Weber (RWTH Aachen) –  Isogeometric Analysis for Micromechanical Analysis of Cast Iron

Material 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.

Hugo Verhelst (TU Delft) – A Parallel Adaptive Arc-Length Method

Parallel 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].
As 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.
In 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.
References
[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.
[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.
[3] Cyr, E. C., Günther, S., & Schroder, J. B. (2019). Multilevel initialization for layer-parallel deep neural network training. arXiv preprint arXiv:1912.08974.

Location

The 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.

Lodging

Here are some suggestions for lodging in Antibes with an easy access by bus to Sophia Antipolis by bus.

Schedule of the buses can be found here :
https://www.envibus.fr/en/bus-routes/ligne-a.html

More Hotels in Sophia Antipolis (from Nice Airport, you can take bus 230 to go to Sophia Antipolis)

 More Hotels in Antibes (10km from Inria – can reach Sophia Antipolis by bus A and bus 9)

 (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)

Transporation

Transportation from Nice Airport

 Transportation from Antibes train station (TGV/Thalys station):

 Transportation from Antibes old town:


Open position at RWTH Aachen University

The GRAPES Network has one (1) open research training position at RWTH Aachen University (Germany) under the supervision of Prof. Leif Kobbelt. The position is for 27 months and *may* lead to a PhD. For details see http://grapes-network.eu/phd-positions/, or contact Prof. Leif Kobbelt. Applications should be submitted via email to sekretariati8@cs.rwth-aachen.de following the instructions at http://grapes-network.eu/phd-positions/how-to-apply/.

Learning Week I: Academic skills and advanced topics in CAD

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.

The 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.

The 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).

You can download a pdf version of the preliminary programe here.

Registration

Online registration is now closed!

Location

The meeting will take place at the Institut de Matematiques of the University of Barcelona.

Gran Via de les Corts Catalanes, 585
08007 Barcelona

All 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/).

How to Arrive

The 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).

The Historic Building is at a short walking distance from Plaça de Catalunya, the city’s nerve center.

Schedule of the Learning Week (last updated 4/09/2021)

pdf version

The event will be broadcasted online via zoom. It will be accessible via this link.

Monday, September 6th, 2021
14:00 Brief introduction of the fellows
16:00 Coffee break
16:30 Marc Noy (Polytechnic University of Catalonia): How to talk and write mathematics, a personal perspective
18:00 End of the day

Tuesday, September 7th, 2021
09:00 Albert Ruiz (Autonomous University of Barcelona) — Advanced Latex [slides]
10:30 Coffee break
11:00 Núria Fagella (University of Barcelona) — Do’s and Don’ts about giving math talks [slides]
13:00 Lunch break
14:30 Jordi Mullor (Centre de Recerca Matematica) — Tips and basics for remote presentations [slides]
16:00 Coffee break
16:30 Conversations with Marta Canadell, PhD in Mathematics and currently working in the Tech Industry
18:30 Welcome reception

Friday, September 10th, 2021
09:00 Oral Expositions
10:30 Coffee break
11:00 Oral Expositions
13:00 End of the Learning Week

Schedule of the Workhsop on Advanced Topics in CAD

pdf version

Wednesday, September 8th, 2021
09:00 Christian Arber (TopSolid): GCS Geometric Constraint System … and CAD Solvers: some Maths behind that [slides]
10:30 Coffee break
11:00 Denys Plakhotnik (ModuleWorks): Geometric problems and software implementation in Computer Aided Manufacturing (CAM)
13:00 Lunch break
14:30 Sandra di Rocco (KTH): Algebraic Geometry of Data
16:00 End of the day

Thursday, September 9th, 2021
09:00 Tamas Varady (Budapest University of Technology and Economics): Multi-sided, multi-connected surface
patches for Computer Aided Design
10:30 Coffee break
11:00 Michael Floater (University of Oslo): Surface parameterization
13:00 Lunch break
14:30 Jiri Kosinka (University of Groningen): Subdivision (and other) surfaces in CAD and graphics
16:00 Coffee break
16:30 Free time for discussions
18:00 Reception

Open PhD position at U. Konstanz

The University of Konstanz is looking for a Phd candidate to start the research in August 2021 on the topic of “Polynomial optimization problems with symmetry” under the scope of POEMA project.

The details and application of this job position is available on POEMA project website.

Doctoral school I & Midterm meeting

Due 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!

Schedule of the Doctoral School I

The 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.

Monday, February 1st, 2021 (Chair: I. Emiris)
11:00 Tutorial [Part A]: Pierre Alliez (Inria & GF): Clustering algorithms and introduction to persistent homology
Abstract: 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.
12:30 Lunch break
14:00 Tutorial [Part B]: Pierre Alliez (Inria & GF): Machine Learning 101 and Neural networks
15:30 Coffee break
17:00 Invited talk:Thierry Chevalier (Airbus): Industrial interest in learning, processing & optimising shapes
18:00 End of the day

Tuesday, February 2nd, 2021 (Chair: M. Salamó)
11:00 Tutorial [Part A]: Yannis Avrithis (Inria & Athena RC): Deep learning and computer vision
Abstract: 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.
12:30 Lunch break
14:00 Tutorial [Part B]: Yannis Avrithis (Inria & Athena RC): Deep learning and computer vision
15:30 Coffee break
16:00 Supervisory Board meeting (restricted)
17:00 End of the day

Wednesday, February 3rd, 2021 (Chair: A. Mantzaflaris)
11:00 Tutorial [Part A]: Michael Bronstein (USI & Imperial College London)
12:30 Coffee break
13:00 Tutorial [Part B]: Michael Bronstein (USI & Imperial College London)
14:30 Lunch break
16:00 “Social” activities: Virtual tours of the Acropolis: #1, #2, and VR 360ᵒ video tour of the Acropolis Museum.

Thursday, February 4th, 2021
Meeting with the REA Project Officer: schedule and its pdf version.

Friday, February 5th, 2021 (Chair: G. Muntingh)
11:00 Tutorial [Part A]: Maria Salamó (UB): Point-cloud analysis and classical Machine Learning techniques
Abstract: 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.
12:30 Lunch break
14:00 Tutorial [Part B]: Simone Balocco (UB): Deep learning approaches
15:30 Coffee break
16:00 Educational Committee meeting (restricted)
16:30 End of the day

Monday, February 8th, 2021
11:00 Tutorial [Part A]: Alexander Leutgeb (RISC-Software): Dynamic Cutting Force Prediction (During Simulation of Machining Processes) (Chair: A. Fabri)
Abstract: 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.
After 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.
12:00 Lunch break
14:00 Tutorial [Part B]: Christoph Hofer (RISC-Software): Dynamic Cutting Force Prediction (During Simulation of Machining Processes) (Chair: A. Fabri)
15:00 Coffee break
17:00 Seminar: Managing and sharing research outputs: all you need to knowSLIDES & VIDEO
Presenters: Elli Papadopoulou, Athena RC/ OpenAIRE, Iryna Kuchma, Electronic Information for Libraries – eifl / OpenAIRE
Abstract: 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.
18:15 End of the Doctoral School


Schedule of the Midterm Meeting (February 4th, 2021)

The 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.

You can find a pdf version of the Midterm meeting program here. All times are Greek local time (EET=UTC+2=GMT+2).

10:00 Introduction by the Project Coordinator and the REA Project Officer
10:05 Tour de table: All scientists-in-charge briefly present their research team and describe their role within the network. Introduction of the Partner Organisations
10:45 REA Project officer presentation
11:05 Coordinator’s report: Presentation of the Network and the Progress report
11:35 Lunch break
13:00 Fellows’ individual presentations
14:00 Coffee break
14:15 Fellows’ individual presentations
15:00 Restricted Meeting between the fellows and the Project Officer
16:00 Coffee break
16:15 Restricted session: Meeting between site-leaders and Project Officer
16:30 Feedback and open discussion
17:00 End of the meeting

1st Doctoral School and Midterm meeting

The First Doctoral School is just around the corner starting Monday February 1st, 2021. The School focuses on machine learning for shapes. There will be introductory short courses and presentations by GRAPES members and other invited high-profile speaker: P. Alliez (Inria & GF), Y. Avrithis (Inria & Athena RC), M. Bronstein (USI and ICL), M. Salamó (UB), A. Leutgeb from industrial partner RISC-Software, and the Advisory Committee member Thierry Chevalier (Airbus). More details can be found at the School’s webpage. A pdf version of the schedule is also available.

The Midterm review meeting with the REA Project Officer is scheduled for Thursday, February 4th, 2021. A pdf version of its program here.

All times are Greek local time (EET=UTC+2=GMT+2).