Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Artificial Neural Networks: Approximation and Learning Theory
Artificial Neural Networks: Approximation and Learning Theory
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Knowledge-Based Approach To Response Surface Modelling in Multifidelity Optimization
Journal of Global Optimization
Survey paper: Optimal experimental design and some related control problems
Automatica (Journal of IFAC)
Evolutionary optimization of catalysts assisted by neural-network learning
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
An experimental methodology for response surface optimization methods
Journal of Global Optimization
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Approximation methods have found an increasing use in the optimization of complex engineering systems. The approximation method provides a 'surrogate' model which, once constructed, can be called instead of the original expensive model for the purposes of optimization. Sensitivity information on the response of interest may be cheaply available in many applications, for example, through a pertubation analysis in a finite element model or through the use of adjoint methods in CFD. This information is included here within the approximation and two strategies for optimization are described. The first involves simply resampling at the best predicted point, the second is based on an expected improvement approach. Further, the use of lower fidelity models together with approximation methods throughout the optimization process is finding increasing popularity. Some of these strategies are noted here and these are extended to include any information which may be available through sensitivities. Encouraging initial results are obtained.