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.
Original language | English (US) |
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Article number | 66 |
Journal | ACM Computing Surveys |
Volume | 55 |
Issue number | 4 |
DOIs | |
State | Published - Apr 30 2023 |
Bibliographical note
Funding Information:This work was supported by NSF grant #1934721 and by DARPA award W911NF-18-1-0027.
Publisher Copyright:
© 2022 Association for Computing Machinery.
Keywords
- Physics-guided
- deep learning
- hybrid
- knowledge integration
- neural networks
- physics-informed
- theory-guided