Published September 12, 2022
| Version v1
Journal article
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Convolutional Auto Encoders and Clustering for Low-dimensional Parametrization of Incompressible Flows
Authors/Creators
- 1. Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg
Description
We provide the complete code base of the presented approaches in the paper.
Jan Heiland and Yongho Kim (2022), Convolutional Auto Encoders and Clustering for Low-dimensional Parametrization of Incompressible Flows, 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS)
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clustering-regime-main.zip
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