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07/05/2023
Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling
We propose a methodology for improving the accuracy of surrogate models ...
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01/26/2023
Gaussian process regression and conditional Karhunen-Loéve models for data assimilation in inverse problems
We present a model inversion algorithm, CKLEMAP, for data assimilation a...
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08/18/2022
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions
In this paper, we develop a physics-informed neural network (PINN) model...
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07/30/2021
Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems
We develop a physics-informed machine learning approach for large-scale ...
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06/17/2021
Physics-informed CoKriging model of a redox flow battery
Redox flow batteries (RFBs) offer the capability to store large amounts ...
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03/04/2021
Time-dependent stochastic basis adaptation for uncertainty quantification
We extend stochastic basis adaptation and spatial domain decomposition m...
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10/09/2020
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids
Real-time state estimation and forecasting is critical for efficient ope...
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10/08/2020
Dynamic mode decomposition for forecasting and analysis of power grid load data
Time series forecasting remains a central challenge problem in almost al...
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05/09/2020
Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning
We propose a novel approach to model viscoelasticity materials using neu...
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12/06/2019
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
Data assimilation for parameter and state estimation in subsurface trans...
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10/09/2019
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
We propose a new forecasting method for predicting load demand and gener...
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07/31/2019
Gaussian Process Regression and Conditional Polynomial Chaos for Parameter Estimation
We present a new approach for constructing a data-driven surrogate model...
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04/17/2019
Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
We use a conditional Karhunen-Loève (KL) model to quantify and reduce un...
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02/18/2019
Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-Dependent Coefficients
We present two approximate Bayesian inference methods for parameter esti...
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04/17/2018
Multivariate Gaussian Process Regression for Multiscale Data Assimilation and Uncertainty Reduction
We present a multivariate Gaussian process regression approach for param...
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03/22/2018
