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)|
|Title of host publication||2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||3|
|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
|Name||International Geoscience and Remote Sensing Symposium (IGARSS)|
|Conference||2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020|
|Period||9/26/20 → 10/2/20|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
Copyright 2021 Elsevier B.V., All rights reserved.
- Deep Learning
- Lake Temperature Modeling
- Physical Systems