ListarDepartament: Matemàtiques por tema "uncertainty quantification"
Mostrando ítems 1-7 de 7
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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 ... -
Lp-calculus Approach to the Random Autonomous Linear Differential Equation with Discrete Delay
Springer Nature Switzerland AG (2019-06-19)In this paper, we provide a full probabilistic study of the random autonomous linear differential equation with discrete delay τ > 0: x (t) = ax(t) + bx(t − τ ), t ≥ 0, with initial condition x(t) = g(t), −τ ≤ t ≤ 0. ... -
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, ... -
Some Notes to Extend the Study on Random Non-Autonomous Second Order Linear Differential Equations Appearing in Mathematical Modeling
MDPI (2018-11-27)The objective of this paper is to complete certain issues from our recent contribution (Calatayud, J.; Cortés, J.-C.; Jornet, M.; Villafuerte, L. Random non-autonomous second order linear differential equations: mean ... -
Uncertainty quantification for random Hamiltonian systems by using polynomial expansions and geometric integrators
Elsevier (2021-07-22)Recent advances in the field of uncertainty quantification are based on achieving suitable functional representations of the solutions to random systems. This aims at improving the performance of Monte Carlo simulation, ... -
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, ...