Physics-Informed Machine Learning

The document provides an introduction to physics-informed machine learning. It discusses the limitations of traditional modeling approaches and machine learning alone. Physics-informed machine learning aims to embed physical laws and constraints into machine learning models. There are three main approaches: incorporating observational biases, inductive biases from physics, and learning biases like physics-informed neural networks (PINNs). PINNs have been applied to problems with complex geometries and different physical laws but can have convergence issues that require further research. Overall, physics-informed machine learning shows promise for improving simulations but many open problems remain.