Deep Embedded Multiplicative DMD for Algebra-Preserving Koopman Learning
- Published
- Source
- arXiv
- Paper number
- 320
- Field
- Machine Learning
- arXiv ID
- 2606.05131
Key points
- Deep Koopman methods learn flexible coordinates, while structure-preserving methods enforce operator identities over a fixed dictionary.
- The result is a finite transition map over the learned latent cells.
- These results suggest a practical rule for Koopman learning: learn the coordinates, constrain the algebra.
Paper links
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