Modeling and nonlinear parameter estimation with Kronecker product representation for coupled oscillators and spatiotemporal systems

DOI10.1016/j.physd.2006.12.006zbMath1120.34006OpenAlexW2069338608MaRDI QIDQ875464

Erik M. Bollt, Chen Yao

Publication date: 13 April 2007

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physd.2006.12.006

zbMATH Keywords

modeling of multivariate time dependent data-set

Mathematics Subject Classification ID

Inverse problems involving ordinary differential equations (34A55)

Related Items (12)

Regularized forecasting of chaotic dynamical systems ⋮ Dimensional implications of dynamical data on manifolds to empirical KL analysis ⋮ Recovery guarantees for polynomial coefficients from weakly dependent data with outliers ⋮ Extracting non-Gaussian governing laws from data on mean exit time ⋮ Data based identification and prediction of nonlinear and complex dynamical systems ⋮ On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions ⋮ How entropic regression beats the outliers problem in nonlinear system identification ⋮ Model selection for hybrid dynamical systems via sparse regression ⋮ Constrained sparse Galerkin regression ⋮ Deep learning of conjugate mappings ⋮ Poincaré maps for multiscale physics discovery and nonlinear Floquet theory ⋮ Bridging the gap: machine learning to resolve improperly modeled dynamics

Cites Work

• All possible chaotic dynamics can be approximated in three dimensions
• MODEL SELECTION, CONFIDENCE AND SCALING IN PREDICTING CHAOTIC TIME-SERIES
• Chaos
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