Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system
In recent years, data-driven deep learning models have gained significant importance in the analysis of turbulent dynamical systems. Within the context of reduced-order models, convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches. They can learn nonl...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-03-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0244416 |
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