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Physics-guided machine learning: A new paradigm for scientific knowledge discovery

Published online by Cambridge University Press:  30 July 2021

Xiaowei Jia
Xiaowei Jia*
Affiliation:
University of Pittsburgh, Sewickley, Pennsylvania, United States

Abstract

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Type
Data Management, Version Control, and Multiformat Analysis in Electron Microscopy
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 1344 - 1345
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Jia, Xiaowei, Willard, Jared, Karpatne, Anuj, Read, Jordan, Zwart, Jacob, Steinbach, Michael, and Kumar, Vipin. “Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles.” In Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 558-566. Society for Industrial and Applied Mathematics, 2019.Google Scholar
Read, Jordan S., Jia, Xiaowei, Willard, Jared, Appling, Alison P., Zwart, Jacob A., Oliver, Samantha K., Karpatne, Anuj, et al. “Process-guided deep learning predictions of lake water temperature.” Water Resources Research ( 2019).CrossRefGoogle Scholar
Jia, Xiaowei, Karpatne, Anuj, Willard, Jared, Steinbach, Michael, Read, Jordan, Hanson, Paul C., Dugan, Hilary A., and Kumar, Vipin. “Physics guided recurrent neural networks for modeling dynamical systems: Application to monitoring water temperature and quality in lakes.” arXiv preprint arXiv:1810.02880 (2018).Google Scholar