Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Reliability Engineering and System Safety Année : 2010

Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation

Résumé

This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dynamical urban drainage simulator that predicts the discharge from a catchment area during a precipitation event is considered. The goal is to perform a global sensitivity analysis and to identify the unknown model parameters as well as the measurement and prediction errors. These objectives can only be achieved by cheapening the incurred computational costs, that is, lowering the number of necessary model runs. With this in mind, a regularity-exploiting metamodeling technique is proposed that enables fast uncertainty quantification. Principal component analysis is used for output dimensionality reduction and sparse polynomial chaos expansions are used for the emulation of the reduced outputs. Sensitivity measures such as the Sobol indices are obtained directly from the expansion coefficients. Bayesian inference via Markov chain Monte Carlo posterior sampling is drastically accelerated.
Fichier principal
Vignette du fichier
RSUQ-2017-010B.pdf (5.76 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01902014 , version 1 (23-10-2018)
hal-01902014 , version 2 (13-11-2019)

Identifiants

Citer

J B Nagel, J Rieckermann, B. Sudret. Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation. Reliability Engineering and System Safety, 2010, 195, ⟨10.1016/j.ress.2019.106737⟩. ⟨hal-01902014v2⟩

Collections

CNRS
51 Consultations
122 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More