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Found 5 papers, 2 papers with code
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft
Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization.
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes
The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems.
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes
Gaussian process based active learning methods for reliability analysis have emerged as a promising way for reducing this computational cost.
An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design
The multi-level, multi-disciplinary and multi-fidelity optimization framework developed at Bombardier Aviation has shown great results to explore efficient and competitive aircraft configurations.
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design
We show the good potential of the approach on a set of numerical experiments.
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