Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
It addresses the need for hybrid approaches in complex science and engineering problems, but is incremental as it reviews existing methods rather than introducing new ones.
The paper provides a structured overview of techniques that integrate physics-based modeling with machine learning for engineering and environmental systems, summarizing applications and methodologies to identify knowledge gaps and future research ideas.
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 paper 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.