Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

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.

Automated summary

There is a growing consensus that solving complex science and engineering problems requires innovative methods that integrate traditional physics-based modeling with advanced machine learning techniques. This article offers a comprehensive overview of such techniques, summarizing their application areas and describing the methodologies used to construct physics-guided ML models and hybrid physics-ML frameworks. Additionally, a taxonomy of existing techniques is provided, revealing knowledge gaps and potential crossovers between disciplines that can inspire future research.