Normal Form Autoencoder, data associated with the publication: ‘Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations’.

doi: 10.4121/14790657.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/14790657
Datacite citation style:
Kalia, Manu; Meijer, Hil; Brunton, Steven L.; Nathan Kutz, J.; Brune, Christoph (2021): Normal Form Autoencoder, data associated with the publication: ‘Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations’. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/14790657.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
University of Twente logo
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licence
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Training and test data sets and neural networks, for the normal form autoencoder: https://arxiv.org/abs/2106.05102
history
  • 2021-06-18 first online, published, posted
publisher
4TU.ResearchData
organizations
University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Applied Analysis (AA);

DATA

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