2011/02/23 by Alexandre Janon, Janon, Alexandre, Maëlle Nodet +3
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Statistics Theory (math.ST) #math.ST #stat.CO #stat.TH
paper · pdf · doi:10.48550/arxiv.1102.4668
arxiv created 2011/02/23 · openalex publication_date 2011/02/23 · arxiv updated 2011/02/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.