Thursday, December 7, 2023
Time | Event | (+) |
08:30 - 09:00 | Welcome coffee | |
09:00 - 09:10 | Opening words - Yves Balkanski, Institut Pascal Director | |
09:10 - 09:30 | Opening session - Marc Schoenauer, Scientific Coordinator, IA2 program – IRT SystemX, Research Director Inria Saclay – Ile-de-France | |
09:30 - 10:30 | Keynote - Nils Thuerey, Technical University of Munich (TUM) - Probabilistic Simulations: Diffusion Models & Differentiable Solvers - Nils Thuerey, Technical University of Munich (TUM) | |
10:30 - 11:00 | Coffee break and posters | |
11:00 - 11:25 | Contributed talks (Session 1) - A Meta-Generation framework for Industrial System Generation - Fouad Oubari, Raphael Meunier, Rodrigue Décatoire, Mathilde Mougeot (ENS-Paris Saclay, Michelin) - A Meta-Generation framework for Industrial System Generation | |
11:25 - 11:50 | Contributed talks (Session 1) - Conditional and Unconditonal Generation of Seismic Signals using Diffusion Model . - Hugo Gabrielidis, Filippo Gatti, Stéphane Vialle (LMPS, LISN) - Conditional and Unconditonal Generation of Seismic Signals using Diffusion Model . | |
11:50 - 12:15 | Contributed talks (Session 1) - Speeding HPC simulation with AI : An Introduction to Physics-informed Machine Learning with NVIDIA Modulus - Francois Courteille (NVIDIA) - Speeding HPC simulation with AI : An Introduction to Physics-informed Machine Learning with NVIDIA Modulus | |
12:15 - 13:45 | Lunch break and posters | |
13:45 - 14:10 | Contributed talks (Session 2) - Approximately well-balanced Discontinuous Galerkin methods using bases enriched with Physics-Informed Neural Networks - Victor Michel-Dansac, Laurent Navoret, Emmanuel Franck (TONUS-Inria Nancy, IRMA) - Approximately well-balanced Discontinuous Galerkin methods using bases enriched with Physics-Informed Neural Networks | |
14:10 - 14:35 | Contributed talks (Session 2) - A self-supervised learning framework to identify the permeability field from images based on physics-informed and convolutional neural networks - John Hanna, José V. Aguado, Sebastien Comas-Cardona, Yves Le Guennec (Ecole Centrale de Nantes, IRT Jules Vernes) - A self-supervised learning framework to identify the permeability field from images based on physics-informed and convolutional neural networks | |
14:35 - 15:00 | Contributed talks (Session 2) - GNN-based Schwarz Preconditioner for Large Scale Problem Solving - Matthieu Nastorg, Marc Schoenauer, Guillaume Charpiat, Thibault Faney, Jean-Marc Gratien, Michele Alessandro Bucci (Inria, IFPEN, Safran Tech) - GNN-based Schwarz Preconditioner for Large Scale Problem Solving | |
15:00 - 15:25 | Contributed talks (Session 2) - Deep Learning Acceleration of Flash Algorithms to Simulate Multicomponent Multiphase Systems. - Paul Mcginn, Chaouki Habchi, Julien Bohbot, Thibault Faney, Bruno Delhom, Jean-Charles De Hemptinne (IFPEN) - Deep Learning Acceleration of Flash Algorithms to Simulate Multicomponent Multiphase Systems. | |
15:25 - 15:55 | Coffee break and posters | |
15:55 - 16:20 | Contributed talks (Session 3) - Sample efficient fluid flow control using neuroevolution guided deep reinforcement learning - Tarun Singh, Laurent Cordier, Ronan Fablet (PPRIME, Lab-STICC) - Sample efficient fluid flow control using neuroevolution guided deep reinforcement learning | |
16:20 - 16:45 | Contributed talks (Session 3) - Solving source- and parameter-dependent PDEs with a multi-input Fourier Neural Operator - Fanny Lehmann, Filippo Gatti, Michaël Bertin, Didier Clouteau (CEA-DAM, LMPS) - Solving source- and parameter-dependent PDEs with a multi-input Fourier Neural Operator | |
16:45 - 17:10 | Contributed talks (Session 3) - Learning operators via Deep Learning models. - Joel Soffo (Airbus, Centrale Nantes) - Learning operators via Deep Learning models. | |
17:10 - 17:20 | Conclusion - Paul Labrogère, CEO, IRT SystemX | |
17:20 - 17:30 | Closing cocktail |