Book ; Online: Auxiliary Functions as Koopman Observables
Data-Driven Analysis of Dynamical Systems via Polynomial Optimization
2023
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 ... ...
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. Comment: We have significantly expanded the presentation. This has improved the presentation and made the paper more readable. Comments welcome |
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Keywords | Mathematics - Dynamical Systems ; Computer Science - Machine Learning ; Mathematics - Optimization and Control |
Publishing date | 2023-03-02 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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