This paper presents a review of the current state-of-the-art of numerical methods for
stochastic computations. The focus is on efficient high-order methods suitable for practical …

The@ first graduate-level textbook to focus on fundamental aspects of numerical methods
for stochastic computations, this book describes the class of numerical methods based on …

In this survey, we present an overview of random differential equations, focusing on strong
solutions and methods for estimation of statistics and densities. We combine classical and …

This paper deals with stochastic spectral methods for uncertainty propagation and
quantification in nonlinear hyperbolic systems of conservation laws. We consider problems …

Reduced models of human arterial networks are an efficient approach to analyze
quantitative macroscopic features of human arterial flows. The justification for such models …

In this paper, we consider hyperbolic systems of conservation laws subject to uncertainties
in the initial conditions and model parameters. In order to solve the underlying uncertain …

The dynamical low-rank approximation (DLRA) is used to treat high-dimensional problems
that arise in such diverse fields as kinetic transport and uncertainty quantification. Even …

Efficient modeling and simulation of uncertainties in computational fluid dynamics (CFD)
remains a crucial challenge. In this paper, we present the first stochastic Galerkin (SG) lattice …

In this paper we propose and analyze a stochastic collocation method for solving the second
order wave equation with a random wave speed and subjected to deterministic boundary …

With inverse problems there are often several unknown distributed parameters of which only
one may be of interest. Since assigning incorrect fixed values to the uninteresting …