The optimal control of problems that are constrained by partial differential equations with uncertainties and with uncertain controls is addressed. The Lagrangian that defines the problem is… (More)
We consider the numerical solution of time-dependent partial differential equations with random coefficients. A spectral approach, called stochastic finite element method, is used to compute the… (More)
This paper presents an overview and comparison of iterative solvers for linear stochastic partial differential equations (PDEs). A stochastic Galerkin finite element discretization is applied to… (More)
Partial differential equations with random coefficients appear for example in reliability problems and uncertainty propagation models. Various approaches exist for computing the stochastic… (More)
This talk focuses on the direct solution of large complex indefinite unsymmetric systems, that arise in acoustic, vibro-acoustic and aero-acoustic simulations. These systems are structurally… (More)
Mathematical models of physical systems often contain parameters with an imprecisely known and uncertain character. It is quite common to represent these parameters by means of random variables.… (More)
A study of the relationship between images of the future and aspirations of nonworking adolescents was undertaken to test the hypothesis that pessimism about the future results in reduced study… (More)
The stochastic Galerkin and stochastic collocation method are two state-of-the-art methods for solving partial differential equations (PDE) containing random coefficients. While the latter method,… (More)
This paper considers the analysis of partial differential equations (PDE) containing multiple random variables. Recently developed collocation methods enable the construction of high-order stochastic… (More)