Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

作者：Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar · 发表于：ACM Computing Surveys · 年份：2022 · DOI：10.1145/3514228 · 被引用次数：737 · 研究领域：Model Reduction and Neural Networks、Computational Physics and Python Applications、Gaussian Processes and Bayesian Inference

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.