Titlebook: Simulation-Driven Design by Knowledge-Based Response Correction Techniques; Slawomir Koziel,Leifur Leifsson Book 2016 Springer Internation

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Nonparametric Response Correction Techniques,er, these methods are normally characterized by a better generalization capability than the parametric techniques (cf. Chap. .). The particular techniques described in this chapter are adaptive response correction, adaptive response prediction, and shape-preserving response prediction. We provide th

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Surrogate-Assisted Design Optimization Using Response Features,terms of the so-called response features whose dependence on the optimization variables is much less nonlinear than for the original responses. Feature-based optimization executed at the level of these feature (or characteristic) points may be computationally much more efficient, even when realized

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Enhancing Response Correction Techniques by Adjoint Sensitivity,on using response correction techniques can be enhanced with adjoint sensitivities. In particular, we start by discussing how to incorporate derivative data into the surrogate modeling and optimization process. Then, we provide the formulations for adjoint-enhanced versions of space mapping (Chap. .

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Book 2016techniques as well as a number of specific methods, including those based on correcting the low-fidelity model response (output space mapping, manifold mapping, adaptive response correction and shape-preserving response prediction), as well as on suitable modification of design specifications. Detai

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