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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

Calatayud-Gregori, Julia; Cortés, J.-C.; Jornet-Sanz, Marc (De Gruyter Open, 2018)

[EN] 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 ...
On the convergence of adaptive gPC for non-linear random difference equations: Theoretical analysis and some practical recommendations

Calatayud-Gregori, Julia; Cortés, J.-C.; Jornet-Sanz, Marc (International Scientific Research Publications MY SDN. BHD., 2018)

[EN] 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, ...
Uncertainty quantification for nonlinear difference equations with dependent random inputs via a stochastic Galerkin projection technique

Calatayud-Gregori, Julia; Cortés, J.-C.; Jornet-Sanz, Marc (Elsevier, 2019-06-30)

[EN] Discrete stochastic systems model discrete response data on some phenomenon with inherent uncertainty. The main goal of uncertainty quantification is to derive the probabilistic features of the stochastic system. This ...

Mostrando ítems 1-3 de 3