1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
Augmented KRnet for density estimation and approximation
Xiaoliang Wan, Kejun Tang
In this work, we have proposed augmented KRnets including both discrete and continuous models. One difficulty in flow-based generative modeling is to maintain the invertibility of…
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…
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…
A hierarchical neural hybrid method for failure probability estimation
Ke Li, Kejun Tang, Jinglai Li +2
Failure probability evaluation for complex physical and engineering systems governed by partial differential equations (PDEs) are computationally intensive, especially when high-di…