Machine Learning: Mathematical Theory and Scientific Applications
Modern machine learning has had remarkable success in all kinds of AI applications, and is also poised to change fundamentally the way we do physical modeling. In this talk, I will give an overview on some of the theoretical and practical issues that I consider most important in this exciting area. The first part of this talk will be focused on the fol-lowing question: How can we make use of modern machine learning tools to help build reliable and practical physi-cal models? Here we will address two issues (mostly using the example of molecular dynamics):
(1) building machine learning models that satisfy physical constraints;
(2) using microscopic models to generate the optimal data set.
