Abstract Multi-fidelity Monte Carlo methods leverage low-fidelity and surrogate models for
variance reduction to make tractable uncertainty quantification even when numerically …

The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as
well as the respective nonlinear saturation mechanisms) are known to be sensitive with …

In many fields of science, comprehensive and realistic computational models are available
nowadays. Often, the respective numerical calculations call for the use of powerful …

Multifidelity methods leverage low-cost surrogate models to speed up computations and
make occasional recourse to expensive high-fidelity models to establish accuracy …

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

A newly developed sensitivity-driven approach is employed to study the role of energetic
particles in suppressing turbulence-inducing micro-instabilities for a set of realistic JET-like …

Multifidelity methods are widely used for estimating quantities of interest (QoI) in
computational science by employing numerical simulations of differing costs and accuracies …

This work combines optimization under uncertainty (OUU) with the multilevel Monte Carlo
(MLMC) method. For MLMC, we present new estimators for variance, standard deviation …

Multi-fidelity Monte Carlo sampling has proven to be an efficient method for quantifying
uncertainty in applications with a large number of stochastic input parameters and …

Bayesian inference is a ubiquitous and flexible tool for updating a belief (ie, learning) about
a quantity of interest given observed data, which ultimately can be used to inform upstream …