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11/11/2020
Teaching deep learning causal effects improves predictive performance
Causal inference is a powerful statistical methodology for explanatory a...
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09/26/2020
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
This paper proposes a physics-guided machine learning approach that comb...
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03/10/2020
Integrating Physics-Based Modeling with Machine Learning: A Survey
In this manuscript, we provide a structured and comprehensive overview o...
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01/28/2020
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Physics-based models of dynamical systems are often used to study engine...
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10/31/2018
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
This paper proposes a physics-guided recurrent neural network model (PGR...
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10/06/2018
Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks
In many domains, there is significant interest in capturing novel relati...
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10/05/2018
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes
In this paper, we introduce a novel framework for combining scientific k...
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12/27/2016
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Data science models, although successful in a number of commercial domai...
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11/15/2016