Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

• URL: http://arxiv.org/abs/2003.04919v6
• Date: Mon, 14 Mar 2022 01:04:10 GMT
• Title: Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
• Authors: Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar
• Abstract summary: There is a growing consensus that solutions to complex science and engineering problems require novel methodologies. This paper provides a structured overview of such techniques. We then provide a taxonomy of these existing techniques, which uncovers knowledge gaps and potential crossovers of methods between disciplines.
• Score: 5.23043130762977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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