In this work we deal with a stochastic optimal Robin boundary control problem constrained
by an advection-diffusion-reaction elliptic equation with advection-dominated term. We …

An optimal experimental set‐up maximizes the value of data for statistical inferences. The
efficiency of strategies for finding optimal experimental set‐ups is particularly important for …

We consider a hyperbolic system with uncertainty in the boundary and initial data. Our aim is
to show that different boundary conditions give different convergence rates of the variance of …

Electronic system design based on deterministic techniques for power-temperature analysis
is, in the context of current and future technologies, both unreliable and inefficient since the …

A linear PDE problem for randomly perturbed domains is considered in an adaptive Galerkin
framework. The perturbation of the domain's boundary is described by a vector valued …

In this paper, the generalized polynomial chaos (gPC) method is extended to solve
nonlinear random delay differential equations (NRDDEs). The error estimation of the method …

In this thesis we focus on PDEs in which some of the parameters are not known exactly but
affected by a certain amount of uncertainty, and hence described in terms of random …

The stochastic Galerkin and collocation methods are used to solve an advection–diffusion
equation with uncertain and spatially varying viscosity. We investigate well-posedness …

This thesis is concerned with the development of reduced basis methods for parametrized
partial differential equations (PPDEs) with stochastic influences. We consider uncertainties …

Model reduction is a technique used to compute fast and accurate approximations of
physical systems' states when they are described through parametric Partial Differential …