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

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