The DOI displayed 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

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

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Categories

• Cognitive Sciences
• Applied Mathematics

Keywords

bifurcations Neural networks normal form

Licence

GPL-3.0

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by Manu Kalia, Hil Meijer, Steven L. Brunton, J. Nathan Kutz, Christoph Brune

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

References

• https://arxiv.org/abs/2106.05102
• https://github.com/dynamicslab/NormalFormAE

Organizations

University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Applied Analysis (AA);