Workshop on Scientific Machine Learning
Strasbourg, France
July 8 - 12, 2024
Provisional schedule
9:00
9:45
10:30
11:00
11:45
12:30
12:45
13:45
14:00
14:45
15:30
16:00
16:45
17:30
9:00 - 9:45
Axel Klawonn
Domain Decomposition Methods and Machine Learning - Two Mutually Beneficial Areas
9:00 - 9:45
Paola Cinnella
Bayesian machine learning and multi-model ensembles for data-driven turbulent flow prediction
9:00 - 9:45
Régis Duvigneau
PINNs for fluid dynamics : opportunities, current issues and possible paths forward
9:45 - 10:30
Janine Weber
A Domain Decomposition-Based CNN-DNN Architecture for Model Parallel Training of Image Recognition Problems
9:45 - 10:30
Alena Shilova
Learning HJB Viscosity Solutions with PINNs for Continuous-Time Reinforcement Learning
9:45 - 10:30
Virginie Grandgirard
Scientific machine learning to optimize plasma turbulence simulations
Coffee break
11:00 - 11:45
Stefano Pagani
Implicit neural representations: from static shapes to spatio-temporal dynamical modeling
11:00 - 11:45
Emmanuel de Bézenac
An operator preconditioning perspective on training in physics-informed machine learning
11:45 - 12:30
Gianluigi Rozza
Accelerating Numerical Simulations by Model Reduction with Scientific and Physics-Informed Machine Learning
11:45 - 12:30
Virginie Ehrlacher
Numerical solution of eigenvalue Schrödinger problems in high dimension using infinite-width two-layer networks
11:45 - 12:30
Mariella Kast
Evolutional neural networks for time-dependent parametrised partial differential equations
11:45 - 12:30
Benedikt Brantner
Crossroads between Geometric Numerical Integration and Machine Learning
Lunch
Lunch
Lunch
13:45 - 14:00
Welcome
Coffee break
15:30 - 17:30
Poster session with snacks and drinks
15:30 - 17:30