“Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems” (2022), by Jared Willard et al. Physics and Astronomy research paper. Original abstract below, unchanged. A free PDF is available.

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Jared Willard, Xiaowei Jia, Shaoming Xu et al. · March 25, 2022 · Physics and Astronomy

Abstract

There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML) techniques. This article provides a structured overview of such techniques. Application-centric objective areas for which these approaches have been applied are summarized, and then classes of methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks are described. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines that can serve as ideas for future research.

Free PDF DOI: 10.1145/3514228