Integrating Machine Learning with Physics-Based Modeling

2020/06/04 by Weinan E, E Weinan, E, Weinan +4 · 26 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · Mathematics · Physics and Astronomy · #Active learning (machine learning) #Artificial intelligence #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #Computational learning theory #Computer science #Data science #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Machine learning #Numerical Analysis (math.NA) #Protein Structure and Dynamics #Simple (philosophy) #cs.LG #cs.NA #math.NA #physics.comp-ph

paper · pdf · doi:10.48550/arxiv.2006.02619

published in arXiv (Cornell University) (Cornell University)

arxiv created 2020/06/04 · openalex publication_date 2020/06/04 · arxiv updated 2020/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Self-Consistent Equations Including Exchange and Correlation Effects
• Less is more: Sampling chemical space with active learning
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
• Learning Multiple Layers of Features from Tiny Images

Related

• Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm
• Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
• Machine learning and the physical sciences
• Machine learning approaches for analyzing and enhancing molecular dynamics simulations
• The Physics of Machine Learning: An Intuitive Introduction for the Physical Scientist