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

Manu Kalia (Creator)
Hil G.E. Meijer (Creator)
Steven L. Brunton (Creator)
J. Nathan Kutz (Creator)
Christoph Brune (Creator)

Applied Analysis

Dataset

Description

Training and test data sets and neural networks, for the normal form autoencoder: https://arxiv.org/abs/2106.05102

License: https://www.gnu.org/licenses/gpl-3.0.html

Date made available	18 Jun 2021
Publisher	4TU.Centre for Research Data

1 Abstract
1 Working paper

Structure preserving implicit shape encoding via flow regularization
Dummer, S., Strisciuglio, N. & Brune, C., 18 Nov 2022. 4 p.
Research output: Contribution to conference › Abstract › Academic

Open Access
File
Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations
Kalia, M., Brunton, S. L., Meijer, H. G. E., Brune, C. & Kutz, J. N., 9 Jun 2021, ArXiv.org, 18 p.
Research output: Working paper

File

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

Description

Research output

Structure preserving implicit shape encoding via flow regularization

Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations

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