Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …

Constructing surrogate models for uncertainty quantification (UQ) on complex partial
differential equations (PDEs) having inherently high-dimensional O (10 n), n≥ 2, stochastic …

Process-induced defects are the leading cause of discrepancies between as-designed and
as-manufactured additive manufacturing (AM) product behavior. Especially for metal lattices …

Many applications across sciences and technologies require a careful quantification of
nondeterministic effects to a system output, for example, when evaluating the system's …

A semi-intrusive approach for robust design optimization is presented. The stochastic
moments of the objective function and constraints are estimated using a Taylor series-based …

In this paper, a dynamic modeling method of motor driven electromechanical system is
presented, and the uncertainty quantification of mechanism motion is investigated based on …

Uncertainty quantification (UQ) of spent nuclear fuel (SNF) is a crucial task that provides
predictions and confidence bounds for important quantities of interest, such as decay heat …

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 …

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 …

Statistical moments estimation is one of the main topics for the analysis of a stochastic
system, but the balance among the accuracy, efficiency, and versatility for different methods …