Bayesian optimization using deep Gaussian processes with applications to aerospace system design

DOI10.1007/s11081-020-09517-8zbMath1473.62107arXiv1905.03350OpenAlexW3035278585MaRDI QIDQ2245694

El-Ghazali Talbi, Mathieu Balesdent, Nouredine Melab, Ali Hebbal, Loïc Brevault

Publication date: 15 November 2021

Published in: Optimization and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.03350

zbMATH Keywords

Gaussian processglobal constrained optimizationBayesian optimizationdeep Gaussian processnon-stationary function

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Gaussian processes (60G15) Nonparametric estimation (62G05)

Related Items (5)

Deep Gaussian process for multi-objective Bayesian optimization ⋮ Shape Optimization and Flow Analysis of Supersonic Nozzles Using Deep Learning ⋮ Bayesian optimization for the spanwise oscillation of a gliding flat plate ⋮ Sequential design of multi-fidelity computer experiments with effect sparsity ⋮ Isovolumetric adaptations to space-filling design of experiments

Uses Software

• BOBYQA
• COBYLA2
• EGO
• TensorFlow
• ORBIT
• GPflow
• Adam
• OpenMDAO

Cites Work

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• On trust region methods for unconstrained minimization without derivatives
• Efficient global optimization algorithm assisted by multiple surrogate techniques
• Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
• Warped Gaussian Processes and Derivative-Based Sequential Designs for Functions with Heterogeneous Variations
• A non-stationary covariance-based Kriging method for metamodelling in engineering design
• ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions
• Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method

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