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03/09/2023
Müntz ball polynomials and Müntz spectral-Galerkin methods for singular eigenvalue problems
In this paper, we introduce a new family of orthogonal systems, termed a...
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09/25/2022
Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator
In the study of subsurface seismic imaging, solving the acoustic wave eq...
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08/25/2022
Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum
This paper applies an idea of adaptive momentum for the nonlinear conjug...
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02/14/2022
A Meshfree Peridynamic Model for Brittle Fracture in Randomly Heterogeneous Materials
In this work we aim to develop a unified mathematical framework and a re...
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07/03/2021
An asymptotically compatible probabilistic collocation method for randomly heterogeneous nonlocal problems
In this paper we present an asymptotically compatible meshfree method fo...
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06/15/2021
Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery
The solvation free energy of organic molecules is a critical parameter i...
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03/19/2021
A Physics-Informed Neural Network Framework For Partial Differential Equations on 3D Surfaces: Time-Dependent Problems
In this paper, we show a physics-informed neural network solver for the ...
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01/13/2021
The l1-l2 minimization with rotation for sparse approximation in uncertainty quantification
This paper proposes a combination of rotational compressive sensing with...
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10/19/2020
A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors
Various noise models have been developed in quantum computing study to d...
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09/03/2020
Augmented Gaussian Random Field: Theory and Computation
We provide a rigorous theoretical foundation for incorporating data of o...
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08/03/2020
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression
We propose a data fusion method based on multi-fidelity Gaussian process...
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04/07/2020
Nonnegativity-Enforced Gaussian Process Regression
Gaussian Process (GP) regression is a flexible non-parametric approach t...
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12/07/2018
When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method
In this work, we propose a framework that combines the approximation-the...
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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
Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence
In this work, we propose a new Gaussian process regression (GPR) method:...
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07/10/2017
