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Association for Computing Machinery (ACM), ACM Computing Surveys, 4(55), p. 1-37, 2022

DOI: 10.1145/3514228

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Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.