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

TL;DR

This paper introduces a shape invariant model approach for efficiently analyzing dynamic simulator outputs in uncertainty and sensitivity studies, reducing computational costs while maintaining prediction accuracy.

Contribution

It applies shape invariant modeling to dynamic simulators, offering an efficient alternative to traditional methods that evaluate each time step independently.

Findings

The method achieves satisfactory predictive performance.

It is independent of the number of time steps.

It outperforms standard single-step approaches.

Abstract

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.