# Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes

## TL;DR

This paper introduces a Gaussian process-based method for efficiently constructing functional risk curves and analyzing the impact of input uncertainties, with confidence bounds and sensitivity analysis, for complex physical models.

## Contribution

It presents a novel approach using Gaussian processes to build risk curves with confidence bounds and explores sensitivity analysis methods for input parameters.

## Key findings

- Gaussian process regression effectively approximates risk curves.
- Confidence bounds quantify approximation uncertainty.
- PLI sensitivity indices reveal input parameter effects.

## Abstract

A functional risk curve gives the probability of an undesirable event as a function of the value of a critical parameter of a considered physical system. In several applicative situations, this curve is built using phenomenological numerical models which simulate complex physical phenomena. To avoid cpu-time expensive numerical models, we propose to use Gaussian process regression to build functional risk curves. An algorithm is given to provide confidence bounds due to this approximation. Two methods of global sensitivity analysis of the models' random input parameters on the functional risk curve are also studied. In particular, the PLI sensitivity indices allow to understand the effect of misjudgment on the input parameters' probability density functions.