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Convolutional Autoencoders, Clustering, and POD for Low-dimensional Parametrization of Flow Equations [source codes]

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2023Embargo end date: 31 Oct 2023 English Publisher:Zenodo
Authors: Yongho Kim;

doi: 10.5281/zenodo.8339925 , 10.5281/zenodo.8340035 , 10.5280/zenodo.8340035

Convolutional Autoencoders, Clustering, and POD for Low-dimensional Parametrization of Flow Equations [source codes]

Abstract

This is a repository of the source code for the paper: Jan Heiland and Yongho Kim (2024) Convolutional Autoencoders, Clustering, and POD for Low-dimensional Parametrization of Flow Equations, Computers & Mathematics with Applications, vol.175, pp.49-61, doi:10.1016/j.camwa.2024.08.032

Due to the large file size, the pretrained model files are not included. For training tasks, we recommend using GPUs.

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Keywords

convolutional autoencoders, linear parameter varying systems, model order reduction, clustering

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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