Neural operators map multiple functions to different functions, possibly in different spaces, unlike standard neural networks. Hence, neural operators allo... Q Cao,S Goswami,GE Karniadakis - 《Nature Machine Intelligence》 被引量: 0发表: 2024年 Fast Solution of Fully Implicit Runge-Kutta and Disc...
Recently, the advent of deep learning has spurred interest in the development of physics-informed neural networks (PINN) for efficiently solving partial differential equations (PDEs), particularly in a parametric setting. Among all different classes of deep neural networks, the convolutional neural ...
Physics-informed neural networks (PINNs) are increasingly employed to replace/augment traditional numerical methods in solving partial differential equations (PDEs). While state-of-the-art PINNs have many attractive features, they approx... H Wang,R Planas,A Chandramowlishwaran,... - 《Computer Met...
PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain - Jianxun-Wang/phygeonet
To demonstrate the benefits of this theory, we present an analytical example and construct a rapid numerical solver for crustal deformation caused by variable fault slip scenarios using physics-informed neural networks, whose mesh-free property is suitable for modeling dislocation potentials. Fault ...