Latent Diffeomorphic Dynamic Mode Decomposition
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Abstract (arXiv)
We present Latent Diffeomorphic Dynamic Mode Decomposition (LDDMD), a new data reduction approach for the analysis of non-linear systems that combines the interpretability of Dynamic Mode Decomposition (DMD) with the predictive power of Recurrent Neural Networks (RNNs). Notably, LDDMD maintains simplicity, which enhances interpretability, while effectively modeling and learning complex non-linear systems with memory, enabling accurate predictions. This is exemplified by its successful application in streamflow prediction.
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References · 6
- 2014NICE: Non-linear Independent Components Estimationcited by 1,058
- 2017Deep learning for universal linear embeddings of nonlinear dynamicscited by 494
- 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
- 2017Learning Koopman Invariant Subspaces for Dynamic Mode Decompositioncited by 158
- 2016Backprop KF: Learning Discriminative Deterministic State Estimatorscited by 47
- 2023Koopman operator learning using invertible neural networkscited by 12
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