Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Xu, Y P"'
Physics-informed neural networks (PINNs) represent a significant advancement in scientific machine learning by integrating fundamental physical laws into their architecture through loss functions. PINNs have been successfully applied to solve various
Externí odkaz:
http://arxiv.org/abs/2407.10836
In this paper, we propose an adaptive finite element method for computing the first eigenpair of the p-Laplacian problem. We prove that starting from a fine initial mesh our proposed adaptive algorithm produces a sequence of discrete first eigenvalue
Externí odkaz:
http://arxiv.org/abs/2402.06889
We develop entropy dissipative higher order accurate local discontinuous Galerkin (LDG) discretizations coupled with Diagonally Implicit Runge-Kutta (DIRK) methods for nonlinear degenerate parabolic equations with a gradient flow structure. Using the
Externí odkaz:
http://arxiv.org/abs/2301.01427
Let $d\nu$ be a measure in $\mathbb{R}^d$ obtained from adding a set of mass points to another measure $d\mu$. Orthogonal polynomials in several variables associated with $d\nu$ can be explicitly expressed in terms of orthogonal polynomials associate
Externí odkaz:
http://arxiv.org/abs/0911.2818