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Simplex‐based adaptive parametric model order reduction for applications in optimization
Author(s) -
A. O. Leite Mateus,
Delinchant Benoit,
Guichon JeanMichel,
A. Vasconcelos João
Publication year - 2017
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2264
Subject(s) - reduction (mathematics) , model order reduction , parametric statistics , adaptive sampling , mathematical optimization , simplex , computer science , simplex algorithm , optimization problem , parametric model , sampling (signal processing) , algorithm , mathematics , monte carlo method , linear programming , projection (relational algebra) , statistics , geometry , filter (signal processing) , computer vision
A new methodology for optimization using parametric reduced order models is introduced. An adaptive scheme to place the expansion points in the reduction step is combined with a smart parametric space sampling in the optimization step to produce very reliable reduced order models. The methodology is illustrated in a demonstrative electromagnetic problem with appreciable gain in computational time. This technique is of general application and can be used to speed up optimization processes in many areas of engineering.