Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Analytical and Computational Aspects of Collaborative Optimization
Analytical and Computational Aspects of Collaborative Optimization
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Advances in Engineering Software - Special issue on design optimization
Multidisciplinary design optimization of mechanisms
Advances in Engineering Software
Advances in Engineering Software
The use of partially converged simulations in building surrogate models
Advances in Engineering Software
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In the course of designing structural assemblies, performing a full optimization is very expensive in terms of computation time. In order or reduce this cost, we propose a multilevel model optimization approach. This paper lays the foundations of this strategy by presenting a method for constructing an approximation of an objective function. This approach consists in coupling a multiparametric mechanical strategy based on the LATIN method with a gradient-based metamodel called a cokriging metamodel. The main difficulty is to build an accurate approximation while keeping the computation cost low. Following an introduction to multiparametric and cokriging strategies, the performance of kriging and cokriging models is studied using one- and two-dimensional analytical functions; then, the performance of metamodels built from mechanical responses provided by the multiparametric strategy is analyzed based on two mechanical test examples.