Resources for Scientific Machine Learning and Data Assimilation

Resources for Neural Emulators and Scientific Machine Learning

Thesis by Felix Kohler has a good overview of this area and his work. More work can be found by Prof. Nils Thuerey and his group at TUM. Their work on emulators also comes with a pytorch package for training neural emulators for PDEs.

Courses in Scientific Machine Learning: https://neu4mes.github.io/teaching/ https://physicsbaseddeeplearning.org/intro.html

Resources for Data Assimilation and Uncertainty Quantification

AI4OCEAN

https://ai-for-ocean.github.io/workshop2026/

Handbook of Bayesian Deep Learning

https://zenodo.org/records/22114549