Epidemiological modelling has assisted in identifying interventions that reduce the impact of
COVID-19. The UK government relied, in part, on the CovidSim model to guide its policy to …

Classical molecular dynamics is a computer simulation technique that is in widespread use
across many areas of science, from physics and chemistry to materials, biology, and …

This paper introduces a surrogate modeling scheme based on Grassmannian manifold
learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The …

We present an algorithm for computing sparse, least squares‐based polynomial chaos
expansions, incorporating both adaptive polynomial bases and sequential experimental …

This article introduces an hp hp‐adaptive multi‐element stochastic collocation method,
which additionally allows to re‐use existing model evaluations during either hh‐or pp …

In this paper, we address discrete linear systems in the frequency domain, where both
frequency and random parameters are considered. Sampling such a system many times is …

Deterministic interpolation and quadrature methods are often unsuitable to address
Bayesian inverse problems depending on computationally expensive forward mathematical …

This work suggests an interpolation-based stochastic collocation method for the non-
intrusive and adaptive construction of sparse polynomial chaos expansions (PCEs). Unlike …

We propose an efficient surrogate modeling technique for uncertainty quantification. The
method is based on a well-known dimension-adaptive collocation scheme. We improve the …

The present work addresses the problems of high-dimensional approximation and
uncertainty quantification in the context of electromagnetic field simulations. In the presence …