Integrating Machine Learning with Physics-Based Modeling

TL;DR

This paper explores how to effectively combine machine learning with physics-based models to create interpretable, reliable scientific models, emphasizing physical constraints and optimal datasets.

Contribution

It provides guidelines and frameworks for integrating machine learning with physics-based modeling, addressing key issues like physical constraints and dataset optimization.

Findings

Successful integration enhances model interpretability and reliability.

Frameworks for combining machine learning with physics are proposed.

Illustrations include molecular dynamics and kinetic equations.

Abstract

Machine learning is poised as a very powerful tool that can drastically improve our ability to carry out scientific research. However, many issues need to be addressed before this becomes a reality. This article focuses on one particular issue of broad interest: How can we integrate machine learning with physics-based modeling to develop new interpretable and truly reliable physical models? After introducing the general guidelines, we discuss the two most important issues for developing machine learning-based physical models: Imposing physical constraints and obtaining optimal datasets. We also provide a simple and intuitive explanation for the fundamental reasons behind the success of modern machine learning, as well as an introduction to the concurrent machine learning framework needed for integrating machine learning with physics-based modeling. Molecular dynamics and moment closure of kinetic equations are used as examples to illustrate the main issues discussed. We end with a general discussion on where this integration will lead us to, and where the new frontier will be after machine learning is successfully integrated into scientific modeling.