Transformers for modeling physical systems

DOI10.1016/j.neunet.2021.11.022zbMath1521.68184arXiv2010.03957OpenAlexW3216107495MaRDI QIDQ6055222

Nicholas Geneva, Nicholas Zabaras

Publication date: 28 September 2023

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.03957

zbMATH Keywords

deep learningtransformersphysical systemssurrogate modelingself-attentionKoopman-based embedding

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Time series analysis of dynamical systems (37M10) General theory of mathematical modeling (00A71) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (3)

PhySR: physics-informed deep super-resolution for spatiotemporal data ⋮ Airfoil-based convolutional autoencoder and long short-term memory neural network for predicting coherent structures evolution around an airfoil ⋮ DNN-MG: a hybrid neural network/finite element method with applications to 3D simulations of the Navier-Stokes equations

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