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arXiv · 2209.13127

Koopman Reduced Order Modeling with Confidence Bounds

Ryan Mohr·Maria Fonoberova·Igor Mezic
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Abstract

This paper introduces a reduced order modeling technique based on Koopman operator theory that gives confidence bounds on the model's predictions. It is based on a data-driven spectral decomposition of the Koopman 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. Additionally, we propose a heuristic algorithm to choose the number of deterministic modes to keep in the model.

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Ryan Mohr, Maria Fonoberova, Igor Mezic. 2022-09-27. Koopman Reduced Order Modeling with Confidence Bounds. https://arxiv.org/abs/2209.13127

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