135 citations · 219 across the 18 of their papers we have counts for
4 papers · 1 filter
Dimension-reduced KRnet maps for high-dimensional Bayesian inverse problems
Yani Feng, Kejun Tang, Xiaoliang Wan +1
We present a dimension-reduced KRnet map approach (DR-KRnet) for high-dimensional Bayesian inverse problems, which is based on an explicit construction of a map that pushes forward…
A domain-decomposed VAE method for Bayesian inverse problems
Zhihang Xu, Yingzhi Xia, Qifeng Liao
Bayesian inverse problems are often computationally challenging when the forward model is governed by complex partial differential equations (PDEs). This is typically caused by exp…
Adaptive deep density approximation for Fokker-Planck equations
Kejun Tang, Xiaoliang Wan, Qifeng Liao
In this paper we present an adaptive deep density approximation strategy based on KRnet (ADDA-KR) for solving the steady-state Fokker-Planck (F-P) equations. F-P equations are usua…
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…