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There is a growing consensus that solutions to complex science andengineering problems require novel methodologies that are able to integratetraditional physics-based modeling approaches with state-of-the-art machinelearning (ML) techniques. This paper provides a structured overview of suchtechniques. Application-centric objective areas for which these approaches havebeen applied are summarized, and then classes of methodologies used toconstruct physics-guided ML models and hybrid physics-ML frameworks aredescribed. We then provide a taxonomy of these existing techniques, whichuncovers knowledge gaps and potential crossovers of methods between disciplinesthat can serve as ideas for future research.

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems