Zobrazeno 1 - 10
of 138
pro vyhledávání: '"Li, Xi'an"'
Bayesian Physics Informed Neural Networks (BPINN) have received considerable attention for inferring differential equations' system states and physical parameters according to noisy observations. However, in practice, Hamiltonian Monte Carlo (HMC) us
Externí odkaz:
http://arxiv.org/abs/2408.09340
To address the sensitivity of parameters and limited precision for physics-informed extreme learning machines (PIELM) with common activation functions, such as sigmoid, tangent, and Gaussian, in solving high-order partial differential equations (PDEs
Externí odkaz:
http://arxiv.org/abs/2310.13947
Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe into solving a class of elliptic partial differential equations(PDEs) wit
Externí odkaz:
http://arxiv.org/abs/2306.13385
Deep learning methods have gained considerable interest in the numerical solution of various partial differential equations (PDEs). One particular focus is physics-informed neural networks (PINN), which integrate physical principles into neural netwo
Externí odkaz:
http://arxiv.org/abs/2306.12749
Publikováno v:
In Artificial Intelligence in Geosciences December 2024 5
Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks
Publikováno v:
In Journal of Computational Physics 1 July 2024 508
Publikováno v:
In Computers and Mathematics with Applications 1 April 2024 159:60-75
For solving a class of block two-by-two real linear system, a new single-step iteration method based on triangular splitting scheme is proposed in this paper. Then the convergence properties of this method are carefully investigated. In addition, we
Externí odkaz:
http://arxiv.org/abs/2005.09835
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Autor:
Hao, Zhitao, Li, Xi'an, An, Mingxiao, Gao, Rongrong, Hu, Wei, Yao, Wang, Zheng, Hong, Zhang, Yutao
Publikováno v:
In Engineering Geology 5 December 2023 326