Program
| Time |
Event |
|
|
13:30 - 14:30
|
Data assimilation and ML - Marc Bocquet |
|
|
14:30 - 15:30
|
Assisting sampling of physical systems and Bayesian Inference with generative models - Marylou Gabrié |
|
|
15:30 - 16:00
|
Coffee break |
|
|
16:00 - 17:00
|
Inferring effective state variables and dynamics from data - Nicolas Brodu |
|
|
17:00 - 18:00
|
Simulation-based inference for the Physical Sciences - Gilles Louppe |
|
|
18:00 - 20:00
|
Wine & cheese with a poster session |
|
| Time |
Event |
|
|
08:30 - 09:00
|
Welcoming coffee |
|
|
09:00 - 10:00
|
Scientific machine learning - Chris Rackauckas |
|
|
10:00 - 11:00
|
An overview of operator learning - Nicolas Boullé |
|
|
11:00 - 11:30
|
Coffee break |
|
|
11:30 - 12:30
|
End-to-end neural data assimilation - Ronan Fablet |
|
|
12:30 - 14:00
|
Lunch break with a poster session |
|
|
14:00 - 15:00
|
Physics Informed Neural Networks: Some Insights and Limitations - Emmanuel de Bézenac |
|
|
15:00 - 16:00
|
Neural ODEs, neural operators, and their efficiency - Julia Gusak |
|
|
16:00 - 16:30
|
Coffee break |
|
|
16:30 - 17:30
|
Learning Time Integration for Long-term Dynamics - David Greenberg |
|
| Time |
Event |
|
|
09:00 - 12:00
|
Hands-on session on SciML with Julia - Chris Rackauckas |
|
|
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