ArticleThe Journal of chemical physics2024
Featurizing Koopman mode decomposition for robust forecasting.
Article in The Journal of chemical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
This article introduces an advanced Koopman mode decomposition (KMD) technique-coined Featurized Koopman Mode Decomposition (FKMD)-that uses delay embedding and a learned Mahalanobis distance to enhance analysis and prediction of high-dimensional dynamical systems. The delay embedding expands the observation space to better capture underlying manifold structures, while the Mahalanobis distance adjusts observations based on the system's dynamics. This aids in featurizing KMD in cases where good features are not a priori known. We show that FKMD improves predictions for a high-dimensional linear oscillator, a high-dimensional Lorenz attractor that is partially observed, and a cell signaling problem from cancer research.
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