Data-driven model reduction and transfer operator approximation. (English) Zbl 1396.37083

The authors establish connections between different data-driven model reduction and transfer operator approximation methods developed independently by the dynamical systems, fluid dynamics, machine learning and molecular dynamics communities. Although the derivations of these methods differ, it is shown that the resulting algorithms share many similarities.


37M10 Time series analysis of dynamical systems
37M25 Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.)
37L65 Special approximation methods (nonlinear Galerkin, etc.) for infinite-dimensional dissipative dynamical systems
34L16 Numerical approximation of eigenvalues and of other parts of the spectrum of ordinary differential operators
68P99 Theory of data
37C30 Functional analytic techniques in dynamical systems; zeta functions, (Ruelle-Frobenius) transfer operators, etc.


Full Text: DOI arXiv


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