Scientific Machine Learning

The listed topics will be covered in the course. The course is partly based on the lecture notes from MIT's 18.337 Parallel Computing and Scientific Machine Learn-ing. book.sciml.ai.

1. Introduction to Scientific Machine Learning (SciML)

2. Physics-Informed Neural Networks

3. Automatic Differentiation and Differentiable Pro-gramming

4. Neural Differential Equations

5. Universal Differential Equations and Symbolic Re-gression

6. Neural Operators