A Second-Order Stochastic Dynamic Mode Decomposition by Richardson Extrapolation

Abstract

Stochastic dynamic mode decomposition (SDMD) approximates the Koopman semigroup of an Itô diffusion by a Galerkin generator matrix on a dictionary of observables, computed from samples of the invariant law and evaluations of the generator, and advances observables with a first-order stochastic Taylor step (SDMD-1). The second-order Taylor step (SDMD-2) also needs the matrix of second generator actions, hence a dictionary with four derivatives, evaluations of a fourth-order differential operator at every sample, and a third matrix. We show that the Richardson extrapolation of two SDMD-1 half steps (SDMD-R) is second-order accurate with the data, the matrices, and the regularity of SDMD-1. One error bound covers exact and empirical Galerkin matrices. It consists of the Galerkin error of the dictionary, which is common to all schemes, a time-stepping term, and a data term of order $m^{-1/2}$ in the sample size $m$. The time-stepping term is $O(\dt^2)$ for SDMD-R and $O(Θ_N\dt+\dt^2)$ for SDMD-2, where $Θ_N$ is the norm of the second-order Galerkin residual $\Proj_N\cA(I-\Proj_N)\cA|_{\cV_N}$. The first-order term is attained and, for self-adjoint generators, vanishes only for generator-invariant dictionaries. For reversible periodic diffusions and trigonometric dictionaries with cutoff $K$, the step restriction $\dt\lesssim K^{-2}$ makes all three schemes contractions, and the error against the Koopman semigroup on $H^\ell$ observables is $O(K^{-(\ell-2)}+\dt^p)$ plus a sampling term of order $K^{d+2}m^{-1/2}$ in dimension $d$, with $p=1$ for SDMD-1 and $p=2$ for SDMD-R and SDMD-2. Numerical experiments confirm that SDMD-R matches or improves on the accuracy of SDMD-2 at the cost of SDMD-1.