Deep convolutional Ritz method: parametric PDE surrogates without labeled data

DOI10.1007/s10483-023-2992-6arXiv2206.04675OpenAlexW4382894964MaRDI QIDQ6132294

Nikolaos Bouklas, Jan N. Fuhg, Teeratorn Kadeethum, H. Yoon, Ashwini Karmarkar

Publication date: 14 July 2023

Published in: AMM. Applied Mathematics and Mechanics. (English Edition) (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2206.04675

zbMATH Keywords

convolutional neural network (CNN)deep energy networkphysics-informed constraint

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Numerical analysis (65-XX) Partial differential equations (35-XX)

Related Items (1)

Preface: machine-learning approaches for computational mechanics

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