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05/06/2023
An adaptive ANOVA stochastic Galerkin method for partial differential equations with random inputs
It is known that standard stochastic Galerkin methods encounter challeng...
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03/01/2023
Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems
We present a dimension-reduced KRnet map approach (DR-KRnet) for high-di...
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02/23/2023
Streaming probabilistic tensor train decomposition
The Bayesian streaming tensor decomposition method is a novel method to ...
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02/22/2023
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
Solving high-dimensional Bayesian inverse problems (BIPs) with the varia...
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11/08/2022
Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions
In many Bayesian inverse problems the goal is to recover a spatially var...
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09/25/2022
Reduced basis stochastic Galerkin methods for partial differential equations with random inputs
We present a reduced basis stochastic Galerkin method for partial differ...
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07/11/2022
Deep neural network based adaptive learning for switched systems
In this paper, we present a deep neural network based adaptive learning ...
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05/04/2022
A deep domain decomposition method based on Fourier features
In this paper we present a Fourier feature based deep domain decompositi...
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03/20/2021
Adaptive deep density approximation for Fokker-Planck equations
In this paper we present a novel adaptive deep density approximation str...
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10/21/2020
Tensor Train Random Projection
This work proposes a novel tensor train random projection (TTRP) method ...
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09/24/2019
D3M: A deep domain decomposition method for partial differential equations
A state-of-the-art deep domain decomposition method (D3M) based on the v...
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08/03/2019
