Projects

A seminar exploring influential papers in physics-informed machine learning, covering Gaussian process PDE solvers, Neural ODEs, Neural Operators, hybrid models, and foundational models for PDEs. Students prepared self-contained tutorials discussing how physical knowledge can be encoded in ML models.

GitHub Repository