Volume 54, pp. 483-498, 2021.

Mathematical analysis of some iterative methods for the reconstruction of memory kernels

Martin Hanke


We analyze three iterative methods that have been proposed in the computational physics community for the reconstruction of memory kernels in a stochastic delay differential equation known as the generalized Langevin equation. These methods use the autocorrelation function of the solution of this equation as input data. Although they have been demonstrated to be useful, a straightforward Laplace analysis does not support their conjectured convergence. We provide more detailed arguments to explain the good performance of these methods in practice. In the second part of this paper we investigate the solution of the generalized Langevin equation with a perturbed memory kernel. We establish sufficient conditions including error bounds such that the stochastic process corresponding to the perturbed problem converges to the unperturbed process in the mean square sense.

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Key words

generalized Langevin equation, Laplace transform, strong convergence

AMS subject classifications

60G15, 65R32, 65C20

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