2006
DOI: 10.1109/tmag.2006.870969
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Manifold-mapping optimization applied to linear actuator design

Abstract: C e n t r u m v o o r W i s k u n d e e n I n f o r m a t i c a MAS Modelling, Analysis and Simulation Modelling, Analysis and SimulationManifold-mapping optimization applied to linear actuator design ABSTRACT Optimization procedures in practice are based on highly accurate models that typically have an excessive computational cost. By exploiting auxiliary models that are less accurate but much cheaper to compute, space-mapping has been reported to accelerate such procedures. However, the space-mapping solutio… Show more

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Cited by 61 publications
(39 citation statements)
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“…We emphasize that the MM-optimization method is not designed for such simple least squares problems, but for problems where the f -evaluation is quite expensive. Examples of such elaborate cases from practice can be found, e.g., in [8]. In the simple examples shown here, the coarse model is not specially adapted to the fine one, and particularly the last example shows what adverse effects can be expected in such cases.…”
Section: Examplesmentioning
confidence: 94%
“…We emphasize that the MM-optimization method is not designed for such simple least squares problems, but for problems where the f -evaluation is quite expensive. Examples of such elaborate cases from practice can be found, e.g., in [8]. In the simple examples shown here, the coarse model is not specially adapted to the fine one, and particularly the last example shows what adverse effects can be expected in such cases.…”
Section: Examplesmentioning
confidence: 94%
“…The proposed approach adapts the objective function to be minimized with the a priori misfit between fine and coarse forward models, in which the modeling error is represented in a stochastic way. The Bayesian approximation error approach is relatively fast and easy to implement compared to the two-level techniques, such as space mapping (Bandler et al, 2008), manifold mapping (Echeverría et al, 2006), two-level refined direct method , etc.…”
Section: A Bayesian Approach For Modeling Error Reductionmentioning
confidence: 99%
“…Relations (6) and (7) can be enforced if the space-mapping surrogate explicitly uses fine model sensitivity information, e.g., as in the following model: (10) where is any space-mapping surrogate with parameters obtained with (4) and…”
Section: Robust Trust-region Space-mapping Algorithmsmentioning
confidence: 99%