Dynamic deep learning based super-resolution for the shallow water equations

Dynamic deep learning based super-resolution for the shallow water equations

Correctly capturing the transition to turbulence in a barotropic instability requires fine spatial resolution. To reduce computational cost, we propose a dynamic super-resolution approach where a transient simulation on a coarse mesh is frequently corrected using a U-net-type neural network. For the...

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Bibliographic Details
Main Authors: Maximilian Witte, Fabrício R Lapolli, Philip Freese, Sebastian Götschel, Daniel Ruprecht, Peter Korn, Christopher Kadow
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
super-resolution
deep learning
convolutional neural network
shallow water equation
galewesky test case
hybrid modeling
Online Access:https://doi.org/10.1088/2632-2153/ada19f
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