Process Guided Deep Learning for Modeling Physical Systems: An Application in Lake Temperature Modeling | IEEE Conference Publication | IEEE Xplore
Scheduled Maintenance: On Tuesday, 25 February, IEEE Xplore will undergo scheduled maintenance from 1:00-5:00 PM ET (1800-2200 UTC). During this time, there may be intermittent impact on performance. We apologize for any inconvenience.

Process Guided Deep Learning for Modeling Physical Systems: An Application in Lake Temperature Modeling


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 More

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:

ISSN Information:

Conference Location: Waikoloa, HI, USA

References

References is not available for this document.