- Published
- 2022-03-25
- Venue
- ACM Computing Surveys, vol. 55, no. 4, pp. 1-37
- Cited by
- 906
- Open access
- Yes, bronze
- DOI
- 10.1145/3514228
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.