Physics-Informed Machine Learning

This repository hosts the tutorials created by the participants of the Physics-Informed Machine Learning seminar.

Short description

In this seminar, we will explore influential papers in the field of physics-informed machine learning. This includes well-established concepts such as Gaussian process-based PDE solvers, Neural ODEs, and Neural Operators, as well as more recent advancements like hybrid models and foundational models for PDE solving. The goal is for you to prepare a self-contained tutorial based on a selected paper, which you will present in a block seminar at the end of the semester. Through these presentations and tutorials, we will discuss how physical knowledge can be encoded into machine learning models and examine the current limitations of these methods.

The full description can be found here: (https://www.mlsustainableenergy.com/teaching/physics-informed-machine-learning/).

Topics

We are looking forward to the following topics:

• Probabilistic Numerical Method of Lines
• SINDy
• Hamiltonian and Lagrangian Neural Networks
• Mesh-Based Simulation with Graph Neural Networks
• PDE Learning with Diffusion Models
• Continuous PDE Dynamics Forecasting with Implicit Neural Representations
• Probabilistic ODE Solvers