ListarDepartament: Matemàtiques por tema "93E03"
Mostrando ítems 1-6 de 6
-
Computational uncertainty quantification for random non-autonomous second order linear differential equations via adapted gPC: a comparative case study with random Fröbenius method and Monte Carlo simulation
De Gruyter (2018-12)This paper presents a methodology to quantify computationally the uncertainty in a class of differential equations often met in Mathematical Physics, namely random non-autonomous second-order linear differential equations, ... -
Improving the Approximation of the First- and Second-Order Statistics of the Response Stochastic Process to the Random Legendre Differential Equation
Springer Nature Switzerland AG (2019-04-16)In this paper, we deal with uncertainty quantification for the random Legendre differential equation, with input coefficient A and initial conditions X0 and X1. In a previous study (Calbo et al. in Comput Math Appl ... -
On the convergence of adaptive gPC for non-linear random difference equations: Theoretical analysis and some practical recommendations
International Scientific Research Publications (2018-06-19)In this paper, the application of adaptive generalized polynomial chaos (gPC) to quantify the uncertainty for non-linear random difference equations is analyzed. It is proved in detail that, under certain assumptions, the ... -
Random non-autonomous second order linear differential equations: mean square analytic solutions and their statistical properties
Springer (2018-10-24)In this paper we study random non-autonomous second order linear differential equations by taking advantage of the powerful theory of random difference equations. The coefficients are assumed to be stochastic processes, ... -
The damped pendulum random differential equation: A comprehensive stochastic analysis via the computation of the probability density function
Elsevier B.V. (2018-08-13)This paper deals with the damped pendulum random differential equation: X¨(t)+2ω0ξX˙(t) + ω 2 0 X(t) = Y(t), t ∈ [0, T ], with initial conditions X(0) = X0 and X˙(0) = X1. The forcing term Y(t) is a stochastic process ... -
Uncertainty quantification for random parabolic equations with nonhomogeneous boundary conditions on a bounded domain via the approximation of the probability density function
John Wiley & Sons, Ltd. (2018-11-14)This paper deals with the randomized heat equation defined on a general bounded interval [L1, L2] and with nonhomogeneous boundary conditions. The solution is a stochastic process that can be related, via changes of variable, ...