Physics-Informed Learning and Large Models Day by GdR IASIS

The integration of physical knowledge into learning models is a rapidly growing field aimed at improving the modeling of complex physical phenomena across a wide range of applications. The “Physics-Informed Learning” working group is organizing a dedicated day to bring together researchers working in this area with a data-driven approach.

The event will focus on foundation models and large models for physics, featuring several invited talks on the topic in the morning. The afternoon will be dedicated to contributed talks, poster presentations with short pitches, and opportunities for informal discussions and networking among participants.

Call for Contributions

They are also opening a call for contributions. Researchers interested in presenting their work are invited to submit a proposal (title and an extended abstract of up to 2 pages) via OpenReview through this link.

Submissions may address, but are not limited to, the following topics: foundation models, datasets, generative models, and applications to various physics domains (computational fluid dynamics, weather forecasting, materials, surfaces, cosmology, computer graphics, etc.).

Submission deadline: May 18.

Invited Speakers

Johannes Brandstetter (Johannes Kepler University Linz)
Guillaume Couairon (Inria Paris)
Laure Raynaud (CNRM Toulouse)

Organizers

Patrick Gallinari, ISIR, Sorbonne University
Amaury Habrard, Hubert Curien Laboratory, Jean Monnet University
Taraneh Sayadi, M2N, CNAM

Venue
Amphi Astier, Esclangon Building, Ground Floor, Sorbonne University, Pierre and Marie Curie Campus, 4 Place Jussieu, 75005 Paris