Auxiliary Functions as Koopman Observables: Data-Driven Analysis of Dynamical Systems via Polynomial Optimization
Author:
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,General Engineering,Modeling and Simulation
Link
https://link.springer.com/content/pdf/10.1007/s00332-023-09990-2.pdf
Reference83 articles.
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2. Ahmadi, A.A., El Khadir, B.: Learning dynamical systems with side information. SIAM Rev. 65(1), 183–223 (2023). https://doi.org/10.1137/20M1388644
3. Bramburger, J.J., Goluskin, D.: Minimum wave speeds in monostable reaction–diffusion equations: sharp bounds by polynomial optimization. Proc. R. Soc. A. 476(2241), 20200450–21 (2020). https://doi.org/10.1098/rspa.2020.0450
4. Bramburger, J.J., Kutz, J.N.: Poincaré maps for multiscale physics discovery and nonlinear Floquet theory. Phys. D 408, 132479–12 (2020). https://doi.org/10.1016/j.physd.2020.132479
5. Brunton, S.L., Brunton, B.W., Proctor, J.L., Kutz, J.N.: Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control. PLoS ONE 11(2), 0150171 (2016a). https://doi.org/10.1371/journal.pone.0150171
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