Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

Jared D Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, Vipin Kumar

Research output: Contribution to journalArticlepeer-review

121 Scopus citations


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.

Original languageEnglish (US)
Article number66
JournalACM Computing Surveys
Issue number4
StatePublished - Nov 21 2022

Bibliographical note

Funding Information:
This work was supported by NSF grant #1934721 and by DARPA award W911NF-18-1-0027.

Publisher Copyright:
© 2022 Association for Computing Machinery.


  • Physics-guided
  • deep learning
  • hybrid
  • knowledge integration
  • neural networks
  • physics-informed
  • theory-guided


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