3rd Physics Informed Machine Learning

This workshop continues discussions and explorations started in January 2016 and January 2018 at the first and second editions of the workshop. A revolution in statistics and machine learning (ML) is underway. Modern algorithms can now learn high level abstractions via hierarchical models, leading to break- through accuracy in benchmarks for computer vision, language, etc. Underlying these advances is a strong and deep connection to various aspects of applied mathematics and statistical physics. For example, proper choice of statistical force allows to screen interaction and learn graphical models governing multi-dimensional distributions efficiently, gauge transformations from physics guide incorporation of symmetries in the neural network design, dynamical system interpretation helps to understand and improve performance of most efficient deep learning schemes, etc. This workshop seeks perspectives on leveraging the deep connection between ML and physics, but now with the goal to better understand and model physical systems, static and dynamic.