Integrating scientific knowledge with machine learning for engineering and environmental systems

J Willard, X Jia, S Xu, M Steinbach, V Kumar - ACM Computing Surveys, 2022 - dl.acm.org
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 …
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Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

JD Willard, X Jia, S Xu, M Steinbach… - ACM Computing …, 2022 - experts.umn.edu
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 …

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

J Willard, X Jia, S Xu, M Steinbach, V Kumar - arXiv preprint arXiv …, 2020 - arxiv.org
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 …

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

J Willard, X Jia, S Xu, M Steinbach, V Kumar - ACM Computing Surveys, 2022 - par.nsf.gov
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 …

Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

J Willard, X Jia, S Xu, M Steinbach, V Kumar - arXiv e-prints, 2020 - ui.adsabs.harvard.edu
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 …
[PDF] arxiv.org

[PDF][PDF] Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

J WILLARD, X JIA, S XU, M STEINBACH… - arXiv preprint arXiv …, 2020 - arxiv.org
Machine learning (ML) models, which have already found tremendous success in
commercial applications, are beginning to play an important role in advancing scientific …