2 citations · 2 across the 3 of their papers we have counts for
7 papers
Adaptive deep density approximation for stochastic dynamical systems
Junjie He, Qifeng Liao, Xiaoliang Wan
In this paper we consider adaptive deep neural network approximation for stochastic dynamical systems. Based on the Liouville equation associated with the stochastic dynamical syst…
Domain-decomposed Bayesian inversion based on local Karhunen-Loève expansions
Zhihang Xu, Qifeng Liao, Jinglai Li
In many Bayesian inverse problems the goal is to recover a spatially varying random field. Such problems are often computationally challenging especially when the forward model is…
A deep domain decomposition method based on Fourier features
Sen Li, Yingzhi Xia, Yu Liu +1
In this paper we present a Fourier feature based deep domain decomposition method (F-D3M) for partial differential equations (PDEs). Currently, deep neural network based methods ar…
Tensor Train Random Projection
Yani Feng, Kejun Tang, Lianxing He +2
This work proposes a novel tensor train random projection (TTRP) method for dimension reduction, where pairwise distances can be approximately preserved. Our TTRP is systematically…
ANOVA Gaussian process modeling for high-dimensional stochastic computational models
Chen Chen, Qifeng Liao
In this paper we present a novel analysis of variance Gaussian process (ANOVA-GP) emulator for models governed by partial differential equations (PDEs) with high-dimensional random…
D3M: A deep domain decomposition method for partial differential equations
Ke Li, Kejun Tang, Tianfan Wu +1
A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs). The solution of PDEs can be form…