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);