Numerical Sensitivity and Efficiency in the Treatment of Epistemic and Aleatory Uncertainty

TL;DR

This paper introduces a numerical sampling method that reduces computational effort in handling both aleatory and epistemic uncertainties modeled by fuzzy random variables.

Contribution

The paper presents a novel sampling approach that improves efficiency in uncertainty quantification involving fuzzy random variables.

Findings

Reduces computational load in uncertainty analysis.

Effective handling of fuzzy random variables.

Potential for broader application in uncertainty quantification.

Abstract

The treatment of both aleatory and epistemic uncertainty by recent methods often requires an high computational effort. In this abstract, we propose a numerical sampling method allowing to lighten the computational burden of treating the information by means of so-called fuzzy random variables.