Zobrazeno 1 - 10
of 230
pro vyhledávání: '"Ying, Wenjun"'
The purpose of this study is to utilize the Chebyshev spectral method neural network(CSNN) model to solve differential equations. This approach employs a single-layer neural network wherein Chebyshev spectral methods are used to construct neurons sat
Externí odkaz:
http://arxiv.org/abs/2407.03347
The kernel-free boundary integral (KFBI) method has successfully solved partial differential equations (PDEs) on irregular domains. Diverging from traditional boundary integral methods, the computation of boundary integrals in KFBI is executed throug
Externí odkaz:
http://arxiv.org/abs/2404.15249
The Kernel-Free Boundary Integral (KFBI) method presents an iterative solution to boundary integral equations arising from elliptic partial differential equations (PDEs). This method effectively addresses elliptic PDEs on irregular domains, including
Externí odkaz:
http://arxiv.org/abs/2404.15242
This paper introduces a second-order method for solving general elliptic partial differential equations (PDEs) on irregular domains using GPU acceleration, based on Ying's kernel-free boundary integral (KFBI) method. The method addresses limitations
Externí odkaz:
http://arxiv.org/abs/2404.14864
Laser plasma instabilities (LPIs) have significant influences on the laser energy deposition efficiency, hot electron generation, and uniformity of irradiation in inertial confined fusion (ICF). In contrast to theoretical analysis of linear developme
Externí odkaz:
http://arxiv.org/abs/2404.14293
Autor:
Wang, Xiaoshuang, Tan, Liwei, Ying, Wenjun, Wang, Enhao, Xiao, Yao, Zhang, Liangqi, Zeng, Zhong
We present a Pressure-Oscillation-Free projection algorithm for large-density-ratio multiphase fluid-structure interaction simulations, implemented on a non-staggered Cartesian grid. The incompressible Navier-Stokes is decoupled with an improved five
Externí odkaz:
http://arxiv.org/abs/2404.14656
We present a novel one-fluid cavitation model of a specific Mie-Gr\"uneisen equation of state(EOS), named polynomial EOS, based on an artificial neural network. Not only the physics-informed equation but also the experimental data are embedded into t
Externí odkaz:
http://arxiv.org/abs/2405.02313
A Stabilized Parametric Finite Element Method for Surface Diffusion with an Arbitrary Surface Energy
We proposed a structure-preserving stabilized parametric finite element method (SPFEM) for the evolution of closed curves under anisotropic surface diffusion with an arbitrary surface energy $\hat{\gamma}(\theta)$. By introducing a non-negative stabi
Externí odkaz:
http://arxiv.org/abs/2404.02083
In the present study, we introduce an advanced reconstruction indicator, deepMTBVD, which is an evolution of the MUSCL-THINC-BVD algorithm\cite{RN2, RN12}. This novel indicator is developed by employing a deep learning neural network to train on nume
Externí odkaz:
http://arxiv.org/abs/2402.03002
Green's function characterizes a partial differential equation (PDE) and maps its solution in the entire domain as integrals. Finding the analytical form of Green's function is a non-trivial exercise, especially for a PDE defined on a complex domain
Externí odkaz:
http://arxiv.org/abs/2401.17172