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Author

David Echeverria

Other affiliations: Stanford University
Bio: David Echeverria is an academic researcher from Centrum Wiskunde & Informatica. The author has contributed to research in topics: Optimization problem & Electromagnetics. The author has an hindex of 13, co-authored 19 publications receiving 638 citations. Previous affiliations of David Echeverria include Stanford University.

Papers
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Journal Article
TL;DR: In this paper, the authors show that space-mapping optimization can be understood as a special case of defect correction iteration, and they introduce the new concept of flexibility of the underlying models.

168 citations

01 Jan 2006
TL;DR: In this paper, an improved version of space mapping, manifold mapping, is proposed to find a precise solution with the same computational efficiency. But the manifold mapping solution does not always coincide with the accurate model optimum.
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 solution does not always coincide with the accurate model optimum. We introduce manifold mapping, an improved version of space mapping that finds this precise solution with the same computational efficiency. By an example in linear actuator design we show that our technique delivers a significant speed-up compared to other optimization schemes

72 citations

Journal ArticleDOI
TL;DR: In this paper, Echeverria and Hemker give a proof of convergence for the manifold-mapping iteration and compare the performances of several variants of the method for some design problems from electromagnetics.
Abstract: In this paper, we analyze in some detail the manifold-mapping optimization technique introduced recently [Echeverria and Hemker in space mapping and defect correction. Comput Methods Appl Math 5(2): 107—136, 2005]. Manifold mapping aims at accelerating optimal design procedures that otherwise require many evaluations of time-expensive cost functions. We give a proof of convergence for the manifold-mapping iteration. By means of two simple optimization problems we illustrate the convergence results derived. Finally, the performances of several variants of the method are compared for some design problems from electromagnetics.

66 citations

Journal ArticleDOI
TL;DR: Manifold mapping is introduced, an improved version of SM that finds this precise solution with the same computational efficiency in linear actuator design and delivers a significant speedup compared to other optimization schemes.
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 (SM) has been reported to accelerate such procedures. However, the SM solution does not always coincide with the accurate model optimum. We introduce manifold mapping, an improved version of SM that finds this precise solution with the same computational efficiency. By two examples in linear actuator design, we show that our technique delivers a significant speedup compared to other optimization schemes

65 citations

Book ChapterDOI
01 Jan 2008
TL;DR: In this article, the authors present the principles of the space-mapping iteration techniques for the efficient solution of optimization problems and also show how space mapping optimization can be understood in the framework of defect correction.
Abstract: In this chapter we present the principles of the space-mapping iteration techniques for the efficient solution of optimization problems. We also show how space-mapping optimization can be understood in the framework of defect correction.

38 citations


Cited by
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01 Apr 2003
TL;DR: The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it as mentioned in this paper, and also presents new ideas and alternative interpretations which further explain the success of the EnkF.
Abstract: The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

2,975 citations

Journal Article
TL;DR: A generic space-mapping optimization algorithm is formulated, explained step-by-step using a simple microstrip filter example, and its robustness is demonstrated through the fast design of an interdigital filter.
Abstract: In this article we review state-of-the-art concepts of space mapping and place them con- textually into the history of design optimization and modeling of microwave circuits. We formulate a generic space-mapping optimization algorithm, explain it step-by-step using a simple microstrip filter example, and then demonstrate its robustness through the fast design of an interdigital filter. Selected topics of space mapping are discussed, including implicit space mapping, gradient-based space mapping, the optimal choice of surrogate model, and tuning space mapping. We consider the application of space mapping to the modeling of microwave structures. We also discuss a software package for automated space-mapping optimization that involves both electromagnetic (EM) and circuit simulators.

327 citations

Journal ArticleDOI
TL;DR: In this article, the authors systematically cover the significant developments of the last decade, including surrogate modeling of electrical machines and direct and stochastic search algorithms for both single and multi-objective design optimization problems.
Abstract: This paper systematically covers the significant developments of the last decade, including surrogate modeling of electrical machines and direct and stochastic search algorithms for both single- and multi-objective design optimization problems. The specific challenges and the dedicated algorithms for electric machine design are discussed, followed by benchmark studies comparing response surface (RS) and differential evolution (DE) algorithms on a permanent-magnet-synchronous-motor design with five independent variables and a strong nonlinear multiobjective Pareto front and on a function with eleven independent variables. The results show that RS and DE are comparable when the optimization employs only a small number of candidate designs and DE performs better when more candidates are considered.

264 citations

Book ChapterDOI
01 Jan 2011
TL;DR: This chapter briefly describes the basics of surrogate-based optimization, various ways of creating surrogate models, as well as several examples of surrogate -based optimization techniques.
Abstract: Objective functions that appear in engineering practice may come from measurements of physical systems and, more often, from computer simulations. In many cases, optimization of such objectives in a straightforward way, i.e., by applying optimization routines directly to these functions, is impractical. One reason is that simulation-based objective functions are often analytically intractable (discontinuous, non-differentiable, and inherently noisy). Also, sensitivity information is usually unavailable, or too expensive to compute. Another, and in many cases even more important, reason is the high computational cost of measurement/simulations. Simulation times of several hours, days or even weeks per objective function evaluation are not uncommon in contemporary engineering, despite the increase of available computing power. Feasible handling of these unmanageable functions can be accomplished using surrogate models: the optimization of the original objective is replaced by iterative re-optimization and updating of the analytically tractable and computationally cheap surrogate. This chapter briefly describes the basics of surrogate-based optimization, various ways of creating surrogate models, as well as several examples of surrogate-based optimization techniques.

248 citations

Journal ArticleDOI
TL;DR: Research on multigrid methods for optimization problems is reviewed and problems considered include shape design, parameter optimization, and optimal control problems governed by partial differential equations of elliptic, parabolic, and hyperbolic type.
Abstract: Research on multigrid methods for optimization problems is reviewed. Optimization problems considered include shape design, parameter optimization, and optimal control problems governed by partial differential equations of elliptic, parabolic, and hyperbolic type.

181 citations