Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles

Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan Read, Jacob Zwart, Michael S Steinbach, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physical models, while generating outputs consistent with physical laws, and achieving good generalizability. Standard RNNs, even when producing superior prediction accuracy, often produce physically inconsistent results and lack generalizability. We further enhance this approach by using a pre-training method that leverages the simulated data from a physics-based model to address the scarcity of observed data. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where mechanistic (also known as process-based) models are used, e.g., power engineering, climate science, materials science, computational chemistry, and biomedicine.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining, SDM 2019
PublisherSociety for Industrial and Applied Mathematics Publications
Pages558-566
Number of pages9
ISBN (Electronic)9781611975673
DOIs
StatePublished - 2019
Event19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada
Duration: May 2 2019May 4 2019

Publication series

NameSIAM International Conference on Data Mining, SDM 2019

Conference

Conference19th SIAM International Conference on Data Mining, SDM 2019
Country/TerritoryCanada
CityCalgary
Period5/2/195/4/19

Bibliographical note

Publisher Copyright:
Copyright © 2019 by SIAM.

Fingerprint

Dive into the research topics of 'Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles'. Together they form a unique fingerprint.

Cite this