Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Tang, Kejun"'
Solving the Boltzmann-BGK equation with traditional numerical methods suffers from high computational and memory costs due to the curse of dimensionality. In this paper, we propose a novel accuracy-preserved tensor-train (APTT) method to efficiently
Externí odkaz:
http://arxiv.org/abs/2405.12524
Surrogate modeling is of great practical significance for parametric differential equation systems. In contrast to classical numerical methods, using physics-informed deep learning methods to construct simulators for such systems is a promising direc
Externí odkaz:
http://arxiv.org/abs/2402.11283
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 flexible meshless discretization and its potential for high-dimension
Externí odkaz:
http://arxiv.org/abs/2305.18702
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 the prior measure to the posterior measure in the latent space. Our a
Externí odkaz:
http://arxiv.org/abs/2303.00573
Parametric optimal control problems governed by partial differential equations (PDEs) are widely found in scientific and engineering applications. Traditional grid-based numerical methods for such problems generally require repeated solutions of PDEs
Externí odkaz:
http://arxiv.org/abs/2302.02076
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 solutions of PDEs and deep generative models are employed to generate new coll
Externí odkaz:
http://arxiv.org/abs/2112.14038
Autor:
Wan, Xiaoliang, Tang, Kejun
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 the transport map, which is often a trade-off between effectiveness an
Externí odkaz:
http://arxiv.org/abs/2105.12866
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 usually high-dimensional and defined on an unbounded domain, which limits
Externí odkaz:
http://arxiv.org/abs/2103.11181
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 constructed through a tensor train (TT) representation with TT-ranks
Externí odkaz:
http://arxiv.org/abs/2010.10797
Autor:
Hong, Mengying, Du, Yushen, Chen, Dongdong, Shi, Yuan, Hu, Menglong, Tang, Kejun, Hong, Zhuping, Meng, Xiangzhi, Xu, Wan, Wu, Gaoqi, Yao, Yuanyuan, Chen, Liubo, Chen, Wenteng, Lau, Chit Ying, Sheng, Li, Zhang, Tian-Hao, Huang, Haigen, Fang, Zheyu, Shen, Yong, Sun, Fangfang, Qian, Jing, Qu, Haibin, Zheng, Shu, Zhang, Suzhan, Ding, Kefeng, Sun, Ren
Publikováno v:
In Science Bulletin 15 August 2023 68(15):1662-1677
