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01/10/2023
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceiv...
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03/03/2022
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
Numerical modeling and simulation have become indispensable tools for ad...
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06/21/2021
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
In this paper, we present a physics-constrained deep neural network (PCD...
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10/15/2020
An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework
Partial differential equations (PDEs) are fundamental for theoretically ...
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10/29/2019
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central...
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04/02/2019
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
We investigate the use of discrete and continuous versions of physics-in...
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11/24/2018
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
In this work, we propose a new Gaussian process regression (GPR)-based m...
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09/10/2018
