Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization

• URL: http://arxiv.org/abs/2303.01483v4
• Date: Mon, 16 Oct 2023 11:51:09 GMT
• Title: Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization
• Authors: Jason J. Bramburger and Giovanni Fantuzzi
• Abstract summary: We present a flexible data-driven method for system analysis that does not require explicit model discovery. The method is rooted in well-established techniques for approxing the Koopman operator from data and is implemented as a semidefinite program that can be solved numerically.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a flexible data-driven method for dynamical system analysis that does not require explicit model discovery. The method is rooted in well-established techniques for approximating the Koopman operator from data and is implemented as a semidefinite program that can be solved numerically. Furthermore, the method is agnostic of whether data is generated through a deterministic or stochastic process, so its implementation requires no prior adjustments by the user to accommodate these different scenarios. Rigorous convergence results justify the applicability of the method, while also extending and uniting similar results from across the literature. Examples on discovering Lyapunov functions, performing ergodic optimization, and bounding extrema over attractors for both deterministic and stochastic dynamics exemplify these convergence results and demonstrate the performance of the method.

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