Can physics-informed neural networks beat the finite element method?

DOI10.1093/IMAMAT/HXAE011MaRDI QIDQ6630849

Jonas Latz, Carola-Bibiane Schönlieb, Urszula Julia Komorowska, Tamara G. Grossmann

Publication date: 31 October 2024

Published in: IMA Journal of Applied Mathematics (Search for Journal in Brave)

zbMATH Keywords

finite element methodpartial differential equationsdeep learningphysics-informed neural networks

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx)

Related Items (1)

Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning

This page was built for publication: Can physics-informed neural networks beat the finite element method?

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6630849)