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A Modified Bisecting K-Means for Approximating Transfer Operators: Application to the Lorenz Equations
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We investigate the convergence behavior of the extended dynamic mode decomposition for constructing a discretization of the continuity equation associated with the Lorenz equations using a nonlinear dictionary of over 1,000,000 terms. The primary objective is to analyze the resulting operator by varying the number of terms in the dictionary and the timescale. We examine what happens when the number of terms of the nonlinear dictionary is varied with respect to its ability to represent the invariant measure, Koopman eigenfunctions, and temporal autocorrelations. The dictionary comprises piecewise constant functions through a modified bisecting k-means algorithm and can efficiently scale to higher-dimensional systems.
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Learning dissipation and instability fields from chaotic dynamics
Row sums of an estimated transition matrix give local inverse dissipation; column maxima give an upper bound on the inverse Jacobian, tested on 1D and 2D chaotic maps.
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