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Regularized dynamic mode decomposition algorithm for time sequence predictions
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DOI：10.1016/j.taml.2024.100555.png)
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Abstract
En 中文
Dynamic Mode Decomposition (DMD) aims at extracting intrinsic mechanisms in a time sequence via linear recurrence relation of its observables, thereby predicting later terms in the sequence. Stability is a major concern in DMD predictions. We adopt a regularized form and propose a Regularized DMD (ReDMD) algorithm to determine the regularization parameter. This leverages stability and accuracy. Numerical tests for Burgers' equation demonstrate that ReDMD effectively stabilizes the DMD prediction while maintaining accuracy. Comparisons are made with the truncated DMD algorithm.
Keywords:
Dynamic mode decomposition
Reduced order modelling
Stability
Regularization
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