Abstract
In this paper, we introduce a new paradigm that combines scientific knowledge within process-based models and machine learning models to advance scientific discovery in many physical systems. We will describe how to incorporate physical knowledge in real-world dynamical systems as additional constraints for training machine learning models and how to leverage the hidden knowledge encoded by existing process-based models. We evaluate this approach on modeling lake water temperature and demonstrate its superior performance using limited training data and the improved generalizability to different scenarios.
Original language | English (US) |
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Title of host publication | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3494-3496 |
Number of pages | 3 |
ISBN (Electronic) | 9781728163741 |
DOIs | |
State | Published - Sep 26 2020 |
Event | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: Sep 26 2020 → Oct 2 2020 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Conference
Conference | 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 |
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Country/Territory | United States |
City | Virtual, Waikoloa |
Period | 9/26/20 → 10/2/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Keywords
- Deep Learning
- Lake Temperature Modeling
- Physical Systems