Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

By Jared D. Willard, Xiaowei Jia, Shaoming Xu, Michael S. Steinbach, Vipin Kumar. ACM Computing Surveys, 2020

728 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/18b0ca3448bd9703bff86c375362b6ad34f797f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. T. Nissen‐Meyer
2. Benjamin Moseley
3. Andrew Markham

In Statistical and Nonlinear Physics

1. T. Nissen‐Meyer
2. Benjamin Moseley
3. Andrew Markham

Citations in the corpus

• Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (2021, background): “A more recent strategy is to blend together physical principles and traditional algorithms with machine learning in a more nuanced way to create more powerful models, in the burgeoning field of scientific machine learning (SciML) [10, 11].”