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</script>We study the convergence of Hermitian Dynamic Mode Decomposition (DMD) to the spectral properties of self-adjoint Koopman operators. Hermitian DMD is a data-driven method that approximates the Koopman operator associated with an unknown nonlinear dynamical system, using discrete-time snapshots. This approach preserves the self-adjointness of the operator in its finite-dimensional approximations. \rev{We prove that, under suitably broad conditions, the spectral measures corresponding to the eigenvalues and eigenfunctions computed by Hermitian DMD converge to those of the underlying Koopman operator}. This result also applies to skew-Hermitian systems (after multiplication by $i$), applicable to generators of continuous-time measure-preserving systems. Along the way, we establish a general theorem on the convergence of spectral measures for finite sections of self-adjoint operators, including those that are unbounded, which is of independent interest to the wider spectral community. We numerically demonstrate our results by applying them to two-dimensional Schrödinger equations.
24 pages, 4 figures. arXiv admin note: text overlap with arXiv:2312.00137
Mathematics - Spectral Theory, FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Mathematics, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), Dynamical Systems (math.DS), Mathematics - Dynamical Systems, Spectral Theory (math.SP), Machine Learning (cs.LG)
Mathematics - Spectral Theory, FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Mathematics, Mathematics - Numerical Analysis, Numerical Analysis (math.NA), Dynamical Systems (math.DS), Mathematics - Dynamical Systems, Spectral Theory (math.SP), Machine Learning (cs.LG)
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