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Bayesian Optimization in Reduced Eigenbases

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Abstract

Parametric shape optimization aims at minimizing a function f(x) where x ∈ X ⊂ Rd is a vector of d Computer Aided Design parameters, representing diverse characteristics of the shap e Ω x . It is common for d to be large, d & 50 , making the optimization diffcult, especially when f is an expensive black-b ox and the use of surrogate-based approaches [1] is mandatory. Most often, the set of considered CAD shapes resides in a manifold of lower dimension where it is preferable to perform the optimization. We uncover it through the Principal Comp onent Analysis of a dataset of n designs, mapped to a high-dimensional shape space via φ : X → Φ ⊂ R D , D d . With a proper choice of φ , few eigenshapes allow to accurately describ e the sample of CAD shap es through their principal comp onents α α α in the eigenbasis V = [ v 1 ,..., v D ] ...
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Dates and versions

emse-02440143 , version 1 (15-01-2020)

Identifiers

  • HAL Id : emse-02440143 , version 1

Cite

David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert. Bayesian Optimization in Reduced Eigenbases. PGMO Days 2019, Dec 2019, Saclay, France. ⟨emse-02440143⟩
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