Second order linear differential equations with analytic uncertainties: stochastic analysis via the computation of the probability density function

[EN] This paper concerns the analysis of random second order linear differential equations. Usually, solving these equations consists of computing the first statistics of the response process, and that task has been an essential goal in the literature. A more ambitious objective is the computation of the solution probability density function. We present advances on these two aspects in the case of general random non-autonomous second order linear differential equations with analytic data processes. The Frobenius method is employed to obtain the stochastic solution in the form of a mean square convergent power series. We demonstrate that the convergence requires the boundedness of the random input coefficients. Further, the mean square error of the Frobenius method is proved to decrease exponentially with the number of terms in the series, although not uniformly in time. Regarding the probability density function of the solution at a given time, which is the focus of the paper, we rely on the law of total probability to express it in closed-form as an expectation. For the computation of this expectation, a sequence of approximating density functions is constructed by reducing the dimensionality of the problem using the truncated power series of the fundamental set. We prove several theoretical results regarding the pointwise convergence of the sequence of density functions and the convergence in total variation. The pointwise convergence turns out to be exponential under a Lipschitz hypothesis. As the density functions are expressed in terms of expectations, we propose a crude Monte Carlo sampling algorithm for their estimation. This algorithm is implemented and applied on several numerical examples designed to illustrate the theoretical findings of the paper. After that, the efficiency of the algorithm is improved by employing the control variates method. Numerical examples corroborate the variance reduction of the Monte Carlo approach. (C) 2020 Elsevier B.V. All rights reserved.This work is supported by the Spanish "Ministerio de Economia y Competitividad'' grant MTM2017-89664-P. Marc Jornet acknowledges the doctorate scholarship granted by PAID, Spain, as well as "Ayudas para movilidad de estudiantes de doctorado de la Universitat Politecnica de Valencia para estancias en 2019'', Spain, for financing his research stay at CMAP. Julia Calatayud acknowledges "Fundacio Ferran Sunyer i Balaguer'', "Institut d'Estudis Catalans'' and the award from "Borses Ferran Sunyer i Balaguer 2019'', Spain for funding her research stay at CMAP. All authors are also grateful to Inria (Centre de Saclay, DeFi Team), which hosted Marc Jornet and Julia Calatayud during their research stays at Ecole Polytechnique. 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