• français
    • English
    français
  • Login
Help
View Item 
  •   Home
  • Institut de Recherche de l’École navale (IRENAV)
  • View Item
  • Home
  • Institut de Recherche de l’École navale (IRENAV)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A Non-Nested Infilling Strategy for Multi-Fidelity based Efficient Global Optimization

Article dans une revue avec comité de lecture
Author
SACHER, Matthieu
LE MAITRE, Olivier
89626 Centre de Mathématiques Appliquées de l'Ecole polytechnique [CMAP]
DUVIGNEAU, Régis
34586 Centre Inria d'Université Côte d'Azur [CRISAM]
ccHAUVILLE, Frederic
13094 Institut de Recherche de l'Ecole Navale [IRENAV]
DURAND, Mathieu
543276 Sirli Innovations [Pornichet]
LOTHODE, Corentin
233702 K-Epsilon

URI
http://hdl.handle.net/10985/21895
DOI
10.1615/Int.J.UncertaintyQuantification.2020032982
Date
2021
Journal
International Journal for Uncertainty Quantification

Abstract

Efficient global optimization (EGO) has become a standard approach for the global optimization of complex systems with high computational costs. EGO uses a training set of objective function values computed at selected input points to construct a statistical surrogate model, with low evaluation cost, on which the optimization procedure is applied. The training set is sequentially enriched, selecting new points, according to a prescribed infilling strategy, in order to converge to the optimum of the original costly model. Multifidelity approaches combining evaluations of the quantity of interest at different fidelity levels have been recently introduced to reduce the computational cost of building a global surrogate model. However, the use of multifidelity approaches in the context of EGO is still a research topic. In this work, we propose a new effective infilling strategy for multifidelity EGO. Our infilling strategy has the particularity of relying on non-nested training sets, a characteristic that comes with several computational benefits. For the enrichment of the multifidelity training set, the strategy selects the next input point together with the fidelity level of the objective function evaluation. This characteristic is in contrast with previous nested approaches, which require estimation of all lower fidelity levels and are more demanding to update the surrogate. The resulting EGO procedure achieves a significantly reduced computational cost, avoiding computations at useless fidelity levels whenever possible, but it is also more robust to low correlations between levels and noisy estimations. Analytical problems are used to test and illustrate the efficiency of the method. It is finally applied to the optimization of a fully nonlinear fluid-structure interaction system to demonstrate its feasibility on real large-scale problems, with fidelity levels mixing physical approximations in the constitutive models and discretization refinements.

Files in this item

Name:
IRENav_IJUQ_2021_SACHER.pdf
Size:
6.419Mb
Format:
PDF
View/Open

Collections

  • Institut de Recherche de l’École navale (IRENAV)

Related items

Showing items related by title, author, creator and subject.

  • A classification approach to efficient global optimization in presence of non-computable domains 
    Article dans une revue avec comité de lecture
    SACHER, Matthieu; DUVIGNEAU, Régis; LE MAÎTRE, Olivier; DURAND, Mathieu; BERRINI, Elisa; ccHAUVILLE, Frederic; ccASTOLFI, Jacques Andre (Springer Verlag (Germany), 2018)
    Gaussian-Process based optimization methods have become very popular in recent years for the global optimization of complex systems with high computational costs. These methods rely on the sequential construction of a ...
  • Flexible hydrofoil optimization for the 35th America's cup with constrained ego method 
    Communication avec acte
    SACHER, Matthieu; DURAND, Mathieu; BERRINI, Elisa; ccHAUVILLE, Frederic; DUVIGNEAU, Régis; LE MAITRE, Olivier; ccASTOLFI, Jacques Andre (2017)
    This paper investigates the use of constrained surrogate models to solve the multi-design optimization problem of a flexible hydrofoil. The surrogate-based optimization (EGO) substitutes the complex objective function of ...
  • Surrogates and Classification approaches for Efficient Global Optimization (EGO) with Inequality Constraints 
    Communication avec acte
    SACHER, Matthieu; DUVIGNEAU, Régis; LE MAITRE, Olivier; DURAND, Mathieu; BERRINI, Elisa; ccHAUVILLE, Frederic; ccASTOLFI, Jacques Andre (2017-05)
    In this work, we compare the use of Gaussian Process (GP) models for the constraints [Schonlau 1997] with a classification approach relying on a Least-Squares Support Vector Machine (LS-SVM) [Suykens and Vandewalle 1999]. ...
  • Efficient optimization procedure in non-linear fluid-structure interaction problem: Application to mainsail trimming in upwind conditions 
    Article dans une revue avec comité de lecture
    SACHER, Matthieu; ccHAUVILLE, Frederic; DUVIGNEAU, Régis; LE MAÎTRE, Olivier; AUBIN, Nicolas; DURAND, Mathieu (Elsevier, 2017)
    This paper investigates the use of Gaussian processes to solve sail trimming optimization problems. The Gaussian process, used to model the dependence of the performance with the trimming parameters, is constructed from a ...
  • Experimental and numerical trimming optimizations for a mainsail in upwind conditions 
    Communication avec acte
    SACHER, Matthieu; ccHAUVILLE, Frederic; DUVIGNEAU, Régis; LE MAITRE, Olivier; AUBIN, Nicolas; DURAND, Mathieu (2016)
    This paper investigates the use of meta-models for optimizing sails trimming. A Gaussian process is used to robustly approximate the dependence of the performance with the trimming parameters to be optimized. The Gaussian ...

Browse

All SAMCommunities & CollectionsAuthorsIssue DateCenter / InstitutionThis CollectionAuthorsIssue DateCenter / Institution

Newsletter

Latest newsletterPrevious newsletters

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales