GitHub

Releases: WeiminLei/U-WNO-U-Net-Enhanced-Wavelet-Neural-Operator-for-Solving-Parametric-Partial-Differential-Equations

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GitHub

Fast Solving Partial Differential Equations via Imitative Fourier Neural Operator

@inproceedings{cao2024fast, title={Fast Solving Partial Differential Equations via Imitative Fourier Neural Operator}, author={Cao, Lulu and Hong, Haokai and Jiang, Min}, booktitle={2024 International ...
IEEE

A Complex-Valued Neural Operator for Solving 2-D Wave Equations Based on Graph Neural Networks

Abstract: In this work, we propose a complex-valued neural operator (CV-NeuralOp) based on graph neural networks (GNNs) to solve 2-D wave equations. Inspired by Green’s function method for solving ...
IEEE

A Data-Driven Neural Network Applied to Solving Maxwell’s Equations: A novel Fourier neural operator approach

Abstract: The Fourier Neural Operator (FNO), a recently proposed neural network designed for solving partial differential equations (PDEs), is being explored to accelerate electromagnetic (EM) ...
JSTOR Daily

On Dynamic Iteration Methods for Solving Time-Periodic Differential Equations

SIAM Journal on Numerical Analysis, Vol. 30, No. 1 (Feb., 1993), pp. 286-303 (18 pages) Dynamic iteration or, more popularly, waveform relaxation has received a great deal of attention as an efficient ...
Quanta Magazine

New Quantum Algorithms Finally Crack Nonlinear Equations

Sometimes, it’s easy for a computer to predict the future. Simple phenomena, such as how sap flows down a tree trunk, are straightforward and can be captured in a few lines of code using what ...
lse

Partial Differential Equations

This course is available on the BSc in Mathematics and Economics, BSc in Mathematics with Data Science, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course ...