Abstract The Multilevel Monte Carlo (MLMC) approach usually works well when estimating
the expected value of a quantity which is a Lipschitz function of intermediate quantities, but if …

Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but
constructing effective control variates can be challenging when the number of samples is …

We present a general variance reduction technique for the estimation of the expectation of a
scalar-valued quantity of interest associated with a family of model evaluations. The key idea …

We design and implement a novel algorithm for computing a multilevel Monte Carlo (MLMC)
estimator of the joint cumulative distribution function (CDF) of a vector-valued quantity of …

We propose a novel approach for estimating conditional or parametric expectations in the
setting where obtaining samples or evaluating integrands is costly. Through the framework …

Heterogeneity and a paucity of measurements of key material properties undermine the
veracity of quantitative predictions of subsurface flow and transport. For such model …

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of
output quantities of complex differential models with random input data, using gradient …

Forward simulation–based uncertainty quantification that studies the distribution of
quantities of interest (QoI) is crucial for computationally robust engineering design and …

In this work, we consider the problem of estimating the probability distribution, the quantile or
the conditional expectation above the quantile, the so called conditional-value-at-risk …

Science and engineering problems subject to uncertainty are frequently both
computationally expensive and feature nonsmooth parameter dependence, making …