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Koopman Reduced Order Modeling with Confidence Bounds by Mohr, Ryan; Fonoberova, Maria; Mezic, Igor is a scholarly article available to read on EtoBox.

What is Koopman Reduced Order Modeling with Confidence Bounds about?

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.

Author

Mohr, Ryan; Fonoberova, Maria; Mezic, Igor

Published

2022

Language

EN