To perform uncertainty, sensitivity or optimization analysis on scalar
variables calculated by a cpu time expensive computer code, a widely accepted
methodology consists in first identifying the most influential uncertain inputs
(by screening techniques), and then in replacing the cpu time expensive model
by a cpu inexpensive mathematical function, called a metamodel. This paper
extends this methodology to the functional output case, for instance when the
model output variables are curves. The screening approach is based on the
analysis of variance and principal component analysis of output curves. The
functional metamodeling consists in a curve classification step, a dimension
reduction step, then a classical metamodeling step. An industrial nuclear
reactor application (dealing with uncertainties in the pressurized thermal
shock analysis) illustrates all these steps
