Koopman Reduced Order Modeling with Confidence Bounds
arXiv (Cornell University)(2022)
Abstract
This paper introduces a reduced order modeling technique based off Koopman operator theory that gives confidence bounds on the model's predictions. It is based on a data-driven spectral decomposition of said operator. The reduced order model is constructed using a finite number of Koopman eigenvalues and modes while the rest of spectrum is treated as a noise process. This noise process is used to extract the confidence bounds.
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Key words
koopman reduced order modeling
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