Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 21, 2022
- Source ID
- 10.1145/3514228
Entities
People
- Jared Willard
- Michael Steinbach
- Shaoming Xu
- Vipin Kumar
- Xiaowei Jia
Organizations
- Defense Advanced Research Projects Agency
- National Science Foundation
- University of Minnesota
- University of Pittsburgh