Physics-Informed Machine Learning: A Survey

"Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications" (2024) https://arxiv.org/abs/2211.08064 :

> We systematically review the recent development of physics-informed machine learning from three perspectives of machine learning tasks, representation of physical prior, and methods for incorporating physical prior. We also propose several important open research problems based on the current trends in the field. We argue that encoding different forms of physical prior into model architectures, optimizers, inference algorithms, and significant domain-specific applications like inverse engineering design and robotic control is far from being fully explored in the field of physics-informed machine learning. We believe that the interdisciplinary research of physics-informed machine learning will significantly propel research progress, foster the creation of more effective machine learning models, and also offer invaluable assistance in addressing long-standing problems in related disciplines.

Re: LLM ml benchmarks; FrontierMath (only 2% of tasks in 2024), TheoremQA: https://news.ycombinator.com/item?id=42097683