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)|
|Journal||ACM Computing Surveys|
|State||Published - Nov 21 2022|
Bibliographical noteFunding Information:
This work was supported by NSF grant #1934721 and by DARPA award W911NF-18-1-0027.
© 2022 Association for Computing Machinery.
- deep learning
- knowledge integration
- neural networks