11 citations · 31 across the 8 of their papers we have counts for
8 papers
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
Yueyang Wang, Xili Wang, Kejun Tang +3
Solving high-dimensional PDE-governed inverse problems is often challenging due to complex non-Gaussian posterior distributions, expensive forward model evaluations, and misspecifi…
Estimating Committor Functions via Deep Adaptive Sampling on Rare Transition Paths
Yueyang Wang, Kejun Tang, Xili Wang +3
The committor functions are central to investigating rare but important events in molecular simulations. It is known that computing the committor function suffers from the curse of…
Deep adaptive sampling for surrogate modeling without labeled data
Xili Wang, Kejun Tang, Jiayu Zhai +2
Surrogate modeling is of great practical significance for parametric differential equation systems. In contrast to classical numerical methods, using physics-informed deep learning…
AONN-2: An adjoint-oriented neural network method for PDE-constrained shape optimization
Xili Wang, Pengfei Yin, Bo Zhang +1
Shape optimization has been playing an important role in a large variety of engineering applications. Existing shape optimization methods are generally mesh-dependent and therefore…
Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
Kejun Tang, Jiayu Zhai, Xiaoliang Wan +1
Solving partial differential equations (PDEs) is a central task in scientific computing. Recently, neural network approximation of PDEs has received increasing attention due to its…
AONN: An adjoint-oriented neural network method for all-at-once solutions of parametric optimal control problems
Pengfei Yin, Guangqiang Xiao, Kejun Tang +1
Parametric optimal control problems governed by partial differential equations (PDEs) are widely found in scientific and engineering applications. Traditional grid-based numerical…
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations
Kejun Tang, Xiaoliang Wan, Chao Yang
In this work we propose a deep adaptive sampling (DAS) method for solving partial differential equations (PDEs), where deep neural networks are utilized to approximate the solution…
FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting
Bi-Ying Yan, Chao Yang, Feng Chen +2
With the goal of predicting the future rainfall intensity in a local region over a relatively short period time, precipitation nowcasting has been a long-time scientific challenge…