Search Results for author:
Found 7 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.
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning
Evolutionary strategies have recently been shown to achieve competing levels of performance for complex optimization problems in reinforcement learning.
Direct-Search for a Class of Stochastic Min-Max Problems
Recent applications in machine learning have renewed the interest of the community in min-max optimization problems.
TREGO: a Trust-Region Framework for Efficient Global Optimization
By following a classical scheme for the trust region (based on a sufficient decrease condition), the proposed algorithm enjoys global convergence properties, while departing from EGO only for a subset of optimization steps.
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.
