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 m...Show MoreMetadata
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.
Date of Conference: 26 September 2020 - 02 October 2020
Date Added to IEEE Xplore: 17 February 2021
ISBN Information: