AISSAI/GAP 2026

Videos

01. Introduction

02. Machine Learning for Scientific Imaging (Julien Mairal)

03. Physics-informed Deep Learning for Inverse Problems (Ching-Yao Lai)

04. Poster Fast-Forward Session

05. AI for Physical Dynamics: from Neural Operators to Foundation Models (Patrick Gallinari)

06. Spherical Flows for Sampling Categorial Distributions (Gabriele Steidl)

07. Mathematics of Diffusion Models and their Applications for Image Inverse Problems (Samuel Hurault)

08. Mathematics of Diffusion Models and their Applications for Image Inverse Problems (demo) (Samuel Hurault)

09. Model-based Neural Networks + Focus on PnP and Multilevel (Nelly Pustelnik)

10. Foundation Models for Life Sciences (Linus Bleistein)

11. Data-Driven High-Dimensional Inverse Problems in Astrophysics (Laurence Perreault-Levasseur)

12. Simulation-based Inference (Jakob Macke)

13. Simulation-based Inference (demo) (Jakob Macke)

14. Foundation Models on the Earth System (Fanny Lehmann)