Latent Diffeomorphic Dynamic Mode Decomposition

• URL: http://arxiv.org/abs/2505.06351v2
• Date: Fri, 01 Aug 2025 13:56:46 GMT
• Title: Latent Diffeomorphic Dynamic Mode Decomposition
• Authors: Willem Diepeveen, Jon Schwenk, Andrea Bertozzi,
• Abstract summary: We present 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)<n> Notably, LDDMD maintains simplicity, which enhances interpretability, while effectively modeling and learning complex non-linear systems with memory, enabling accurate predictions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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