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pro vyhledávání: '"Cao, Wenbo"'
In light of the challenges surrounding convergence and error propagation encountered in Reynolds-averaged Navier-Stokes (RANS) equations with data-driven Reynolds stress closures, researchers commonly attribute these issues to ill-conditioning throug
Externí odkaz:
http://arxiv.org/abs/2405.02622
Autor:
Cao, Wenbo, Zhang, Weiwei
Physics-informed neural networks (PINNs) have recently emerged as a novel and popular approach for solving forward and inverse problems involving partial differential equations (PDEs). However, achieving stable training and obtaining correct results
Externí odkaz:
http://arxiv.org/abs/2405.01957
Publikováno v:
Physics of Fluids 36, 027134 (2024)
Physics-informed neural networks (PINNs) have recently become a new popular method for solving forward and inverse problems governed by partial differential equations (PDEs). However, in the flow around airfoils, the fluid is greatly accelerated near
Externí odkaz:
http://arxiv.org/abs/2401.08705
Publikováno v:
Journal of Computational Physics 516, 113285, 2024
Engineering problems often involve solving partial differential equations (PDEs) over a range of similar problem setups with various state parameters. In traditional numerical methods, each problem is solved independently, resulting in many repetitiv
Externí odkaz:
http://arxiv.org/abs/2401.07203
Physics-informed neural networks (PINNs) have shown remarkable prospects in the solving the forward and inverse problems involving partial differential equations (PDEs). The method embeds PDEs into the neural network by calculating PDE loss at a seri
Externí odkaz:
http://arxiv.org/abs/2401.06196
Autor:
Cao, Wenbo, Zhang, Weiwei
Deep neural networks (DNNs), especially physics-informed neural networks (PINNs), have recently become a new popular method for solving forward and inverse problems governed by partial differential equations (PDEs). However, these methods still face
Externí odkaz:
http://arxiv.org/abs/2310.16491
Publikováno v:
Physics of Fluids 35, 036124 (2023)
Iterative steady-state solvers are widely used in computational fluid dynamics. Unfortunately, it is difficult to obtain steady-state solution for unstable problem caused by physical instability and numerical instability. Optimization is a better cho
Externí odkaz:
http://arxiv.org/abs/2212.03183
Publikováno v:
In Coordination Chemistry Reviews 1 September 2024 514
Publikováno v:
In Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 5 July 2024 315
In this paper, a turbulence model based on deep neural network is developed for turbulent flow around airfoil at high Reynolds numbers. According to the data got from the Spalart-Allmaras (SA) turbulence model, we build a neural network model that ma
Externí odkaz:
http://arxiv.org/abs/2111.13469