Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

TL;DR

This paper reviews methods that combine physics-based models with machine learning to solve complex engineering and environmental problems, highlighting current approaches, taxonomy, and future research directions.

Contribution

It provides a structured overview and taxonomy of physics-guided and hybrid physics-ML models, identifying knowledge gaps and cross-disciplinary opportunities.

Findings

Summarizes application areas of physics-ML integration.

Classifies methodologies used in hybrid models.

Highlights knowledge gaps and future research directions.

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 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.