Bayesian Optimization in Reduced Eigenbases

Abstract : Parametric shape optimization aims at minimizing a function f ( x ) where x ∈ X ⊂ R d is a vector of d Computer Aided Design parameters, representing diverse characteristics of the shape Ω x . It is common for d to be large, d > 50 , making the optimization diffcult, especially when f is an expensive black-box 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 p erform 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 ...
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Contributor : Florent Breuil <>
Submitted on : Wednesday, November 6, 2019 - 11:36:42 AM
Last modification on : Monday, January 20, 2020 - 12:14:07 PM


  • HAL Id : emse-02351036, version 1


David Gaudrie, Rodolphe Le Riche, Victor Picheny. Bayesian Optimization in Reduced Eigenbases. PGMO Days 2019, Dec 2019, Palaiseau, France. ⟨emse-02351036⟩



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