J
Jared Willard
Researcher at University of Minnesota
Publications - 19
Citations - 944
Jared Willard is an academic researcher from University of Minnesota. The author has contributed to research in topics: Computer science & Mean squared error. The author has an hindex of 6, co-authored 14 publications receiving 375 citations. Previous affiliations of Jared Willard include United States Geological Survey.
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Integrating Physics-Based Modeling with Machine Learning: A Survey
TL;DR: An overview of techniques to integrate machine learning with physics-based modeling and classes of methodologies used to construct physics-guided machine learning models and hybrid physics-machine learning frameworks from a machine learning standpoint is provided.
Book ChapterDOI
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
Xiaowei Jia,Jared Willard,Anuj Karpatne,Jordan S. Read,Jacob A. Zwart,Michael Steinbach,Vipin Kumar +6 more
TL;DR: It is shown that a PGRNN can improve prediction accuracy over that of physical models, while generating outputs consistent with physical laws, and achieving good generalizability.
Journal ArticleDOI
Process-Guided Deep Learning Predictions of Lake Water Temperature
Jordan S. Read,Xiaowei Jia,Jared Willard,Alison P. Appling,Jacob A. Zwart,Samantha K. Oliver,Anuj Karpatne,Gretchen J. A. Hansen,Paul C. Hanson,William Watkins,Michael Steinbach,Vipin Kumar +11 more
TL;DR: In this paper, the authors presented the results of the North Central Climate Adaptation Science Center (NCAACS) at the University of Minnesota (U.M. System) with the help of the National Science Foundation (NSF).
Posted Content
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Xiaowei Jia,Jared Willard,Anuj Karpatne,Jordan S. Read,Jacob A. Zwart,Michael Steinbach,Vipin Kumar +6 more
TL;DR: This article proposed a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improves the modeling of physical processes.
Journal ArticleDOI
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Xiaowei Jia,Jared Willard,Anuj Karpatne,Jordan S. Read,Jacob A. Zwart,Michael Steinbach,Vipin Kumar +6 more
TL;DR: It is shown that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws, and is applicable more widely to a range of scientific and engineering disciplines where physics- based models are used.