Data-driven stochastic inversion via functional quantization

Abstract

In this paper, we propose a new methodology for solving stochastic inversion problems through computer experiments, the stochasticity being driven by a functional random variables. This study is motivated by an automotive application. In this context, the simulator code takes a double set of simulation inputs: deterministic control variables and functional uncertain variables. This framework is characterized by two features. The first one is the high computational cost of simulations. The second is that the probability distribution of the functional input is only known through a finite set of realizations. In our context, the inversion problem is formulated by considering the expectation over the functional random variable. We aim at solving this problem by evaluating the model on a design, whose adaptive construction combines the so-called stepwise uncertainty reduction methodology with a strategy for an efficient expectation estimation. Two greedy strategies are introduced to sequentially estimate the expectation over the functional uncertain variable by adaptively selecting curves from the initial set of realizations. Both of these strategies consider functional principal component analysis as a dimensionality reduction technique assuming that the realizations of the functional input are independent realizations of the same continuous stochastic process. The first strategy is based on a greedy approach for functional data-driven quantization, while the second one is linked to the notion of space-filling design. Functional PCA is used as an intermediate step. For each point of the design built in the reduced space, we select the corresponding curve from the sample of available curves, thus guaranteeing the robustness of the procedure to dimension reduction. The whole methodology is illustrated and calibrated on an analytical example. It is then applied on the automotive industrial test case where we aim at identifying the set of control parameters leading to meet the pollutant emission standards of a vehicle.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the associate editor for their helpful comments which substantially improved this paper. We also thank the Inria Associate Team UNcertainty QUantification is ESenTIal for OceaNic & Atmospheric flows proBLEms. This work was supported by IFPEN and the OQUAIDO chair.

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Correspondence to Mohamed Reda El Amri.

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El Amri, M.R., Helbert, C., Lepreux, O. et al. Data-driven stochastic inversion via functional quantization. Stat Comput 30, 525–541 (2020). https://doi.org/10.1007/s11222-019-09888-8

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Keywords

  • Functional random variable
  • Karhunen–Loève expansion
  • Data reduction
  • Functional quantization
  • Set estimation
  • Gaussian process models