Confidence intervals for sensitivity indices using reduced-basis   metamodels
  Global sensitivity analysis is often impracticable for complex and time demanding numerical models, as it requires a large number of runs. The reduced-basis approach provides a way to replace the original model by a much faster to run code. In this paper, we are interested in the information loss induced by the approximation on the estimation of sensitivity indices. We present a method to provide a robust error assessment, hence enabling significant time savings without sacrifice on precision and rigourousness. We illustrate our method with an experiment where computation time is divided by a factor of nearly 6. We also give directions on tuning some of the parameters used in our estimation algorithms. 
Alexandre Janon (INRIA Rh\^one-Alpes / LJK Laboratoire Jean Kuntzmann,   - M\'ethodes d'Analyse Stochastique des Codes et Tr...
stat.CO math.ST stat.TH