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

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

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, via adaptive generalized Polynomial Chaos (gPC) and the stochastic Galerkin p...

Full description

Bibliographic Details
Main Authors: Calatayud Julia, Cortés Juan Carlos, Jornet Marc
Format: Article
Language:English
Published: De Gruyter 2018-12-01
Series:Open Mathematics
Subjects:
non-autonomous and random dynamical systems
computational uncertainty quantification
adaptive generalized polynomial chaos
stochastic galerkin projection technique
random fröbenius method
34f05
60h35
93e03
Online Access:https://doi.org/10.1515/math-2018-0134

Internet

https://doi.org/10.1515/math-2018-0134

Similar Items