Shape invariant model approach for functional data analysis in uncertainty and sensitivity studies

Ekaterina Sergienko (1, 2, 3) , Fabrice Gamboa (2, 1) , Daniel Busby (3)

1 GdR MASCOT-NUM - Méthodes d'Analyse Stochastique des Codes et Traitements Numériques
2 IMT - Institut de Mathématiques de Toulouse UMR5219
3 IFPEN - IFP Energies nouvelles

Dynamic simulators model systems evolving over time. Often, it operates iteratively over fixed number of time-steps. The output of such simulator can be considered as time series or discrete functional outputs. Metamodeling is an e ective method to approximate demanding computer codes. Numerous metamodeling techniques are developed for simulators with a single output. Standard approach to model a dynamic simulator uses the same method also for multi-time series outputs: the metamodel is evaluated independently at every time step. This can be computationally demanding in case of large number of time steps. In some cases, simulator outputs for di erent combinations of input parameters have quite similar behaviour. In this paper, we propose an application of shape invariant model approach to model dynamic simulators. This model assumes a common pattern shape curve and curve-specific di erences in amplitude and timing are modelled with linear transformations. We provide an e cient algorithm of transformation parameters estimation and subsequent prediction algorithm. The method was tested with a CO2 storage reservoir case using an industrial commercial simulator and compared with a standard single step approach. The method provides satisfactory predictivity and it does not depend on the number of involved time steps.

Mots clés: Kriging; Functional data analysis; Semiparametric model

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Ekaterina Sergienko, Fabrice Gamboa, Daniel Busby. Shape invariant model approach for functional data analysis in uncertainty and sensitivity studies. 2012. ⟨hal-00806562⟩