K
Kaj Madsen
Researcher at Technical University of Denmark
Publications - 20
Citations - 1129
Kaj Madsen is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Space mapping & Engineering optimization. The author has an hindex of 14, co-authored 20 publications receiving 1072 citations. Previous affiliations of Kaj Madsen include University of Copenhagen.
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Journal ArticleDOI
A Space-Mapping Framework for Engineering Optimization—Theory and Implementation
TL;DR: A comprehensive approach to engineering design optimization exploiting space mapping (SM) using a new generalization of implicit SM to minimize the misalignment between the coarse and fine models of the optimized object over a region of interest.
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Space Mapping With Adaptive Response Correction for Microwave Design Optimization
TL;DR: An adaptive response correction scheme is presented to work in conjunction with space-mapping optimization algorithms and is designed to alleviate the difficulties of the standard output space mapping by adaptive adjustment of the response correction term according to the changes of thespace-mapped coarse model response.
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Quality assessment of coarse models and surrogates for space mapping optimization
TL;DR: Methods of assessing the quality of coarse/surrogate models are provided, derived from convergence results for space mapping algorithms, to predict whether a given model might be successfully used in space mapping optimization, or to choose the proper type of space mapping which would be suitable to a given engineering design problem.
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An Introduction to the Space Mapping Technique
TL;DR: The space mapping technique may be considered a preprocessing technique that perhaps must be succeeded by use of classical optimization techniques, and an automatic scheme which integrates the space mapping and classical techniques is presented.
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Space mapping optimization of microwave circuits exploiting surrogate models
TL;DR: A powerful new Aggressive Space Mapping (ASM) optimization algorithm is presented in this paper, which draws upon recent developments in both surrogate-based optimization and microwave device neuromodeling.