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

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

Dataset

• Cognitive Sciences
• Applied Mathematics

bifurcations, Neural networks, normal form

GPL-3.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

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

• 2021-06-18 first online, published, posted

4TU.ResearchData

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

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