most citedA data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction

1 citations · 1 across the 1 of their papers we have counts for

collaborators
Showing math.NAShow all

3 papers · 1 filter

math.NA2021

A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems

Sijing Li, Cheng Zhang, Zhiwen Zhang +1

In this paper, we consider a Bayesian inverse problem modeled by elliptic partial differential equations (PDEs). Specifically, we propose a data-driven and model-based approach to…

math.NA2019

Solving high-dimensional nonlinear filtering problems using a tensor train decomposition method

Sijing Li, Zhongjian Wang, Stephen S. T. Yau +1

In this paper, we propose an efficient numerical method to solve high-dimensional nonlinear filtering (NLF) problems. Specifically, we use the tensor train decomposition method to…

math.NA2019★ 1 cited

A data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction

Sijing Li, Zhiwen Zhang, Hongkai Zhao

We propose a data-driven approach to solve multiscale elliptic PDEs with random coefficients based on the intrinsic low dimension structure of the underlying elliptic differential…