8 citations · 15 across the 8 of their papers we have counts for
8 papers
BlinDNO: A Distributional Neural Operator for Dynamical System Reconstruction from Time-Label-Free data
Zhijun Zeng, Junqing Chen, Zuoqiang Shi
We study an inverse problem for stochastic and quantum dynamical systems in a time-label-free setting, where only unordered density snapshots sampled at unknown times drawn from an…
An Efficient Conditional Score-based Filter for High Dimensional Nonlinear Filtering Problems
Zhijun Zeng, Weiye Gan, Junqing Chen +1
In many engineering and applied science domains, high-dimensional nonlinear filtering is still a challenging problem. Recent advances in score-based diffusion models offer a promis…
Kernel Variational Inference Flow for Nonlinear Filtering Problem
Weiye Gan, Zhijun Zeng, Junqing Chen +1
We present a novel particle flow for sampling called kernel variational inference flow (KVIF). KVIF do not require the explicit formula of the target distribution which is usually…
Neumann Series-based Neural Operator for Solving Inverse Medium Problem
Ziyang Liu, Fukai Chen, Junqing Chen +2
The inverse medium problem, inherently ill-posed and nonlinear, presents significant computational challenges. This study introduces a novel approach by integrating a Neumann serie…
NSNO: Neumann Series Neural Operator for Solving Helmholtz Equations in Inhomogeneous Medium
Fukai Chen, Ziyang Liu, Guochang Lin +2
In this paper, we propose Neumann Series Neural Operator (NSNO) to learn the solution operator of Helmholtz equation from inhomogeneity coefficients and source terms to solutions.…
BI-GreenNet: Learning Green's functions by boundary integral network
Guochang Lin, Fukai Chen, Pipi Hu +4
Green's function plays a significant role in both theoretical analysis and numerical computing of partial differential equations (PDEs). However, in most cases, Green's function is…
M2N: Mesh Movement Networks for PDE Solvers
Wenbin Song, Mingrui Zhang, Joseph G. Wallwork +8
Mainstream numerical Partial Differential Equation (PDE) solvers require discretizing the physical domain using a mesh. Mesh movement methods aim to improve the accuracy of the num…
BINet: Learning to Solve Partial Differential Equations with Boundary Integral Networks
Guochang Lin, Pipi Hu, Fukai Chen +4
We propose a method combining boundary integral equations and neural networks (BINet) to solve partial differential equations (PDEs) in both bounded and unbounded domains. Unlike e…