The researchers found that comparisons between machine learning methods for solving fluid-related PDEs and traditional methods are often biased in favor of machine learning methods. They also ...

There has been a growing interest in efficient numerical algorithms based on tensor networks and low-rank techniques to approximate high-dimensional functions and compute the numerical solution of ...

This repo contains a set of tutorials to learn how to solve partial differential equations (PDEs) in Julia with the Gridap ecosystem of packages. At the root of this ecosystem is the Gridap.jl library ...

This repo is the official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network" by Longxiang Jiang, Liyuan Wang, Xinkun Chu, Yonghao Xiao, and Hao Zhang ...

This course adresses the mathematical theory of discretizations of partial differential equations (PDEs), and covers recent research activity related to this topic. The specific selection of topics ...

This seminar is devoted to the analysis of Partial Differential Equations and their applications. Unless otherwise specified, the Applied PDEs seminars take place in Huxley 658 on Thursdays 14.00 - 15 ...

Research interests in the Analysis and PDEs group currently include spectral theory, inverse problems, harmonic analysis on manifolds, PDEs and their numerical approximation and geometric analysis. Dr ...

Abstract: We consider strong approximations to parabolic stochastic PDEs. We assume the noise lies in a Gevrey space of analytic functions. This type of stochastic forcing includes the case of forcing ...

In recent years, there has evolved a symbiotic and productive relationship between fully nonlinear partial differential equations and generalized potential theories. This book examines important ...