Optimization of scattering resonances

P. Heider, D. Berebichez, Robert Kohn, M. I. Weinstein

Research output: Contribution to journalArticle

Abstract

The increasing use of micro- and nano-scale components in optical, electrical, and mechanical systems makes the understanding of loss mechanisms and their quantification issues of fundamental importance. In many situations, performance-limiting loss is due to scattering and radiation of waves into the surrounding structure. In this paper, we study the problem of systematically improving a structure by altering its design so as to decrease the loss. We use sensitivity analysis and local gradient optimization, applied to the scattering resonance problem, to reduce the loss within the class of piecewise constant structures. For a class of optimization problems where the material parameters are constrained by upper and lower bounds, it is observed that an optimal structure is piecewise constant with values achieving the bounds.

Original languageEnglish (US)
Pages (from-to)443-456
Number of pages14
JournalStructural and Multidisciplinary Optimization
Volume36
Issue number5
DOIs
StatePublished - Nov 2008

Fingerprint

Scattering
Optimization
Resonance Problem
Scattering Problems
Mechanical Systems
Quantification
Sensitivity analysis
Sensitivity Analysis
Upper and Lower Bounds
Limiting
Radiation
Gradient
Optimization Problem
Decrease
Class

Keywords

  • Quality factor
  • Scattering resonance
  • Sensitivity analysis

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Engineering (miscellaneous)
  • Computational Mechanics
  • Mechanics of Materials

Cite this

Optimization of scattering resonances. / Heider, P.; Berebichez, D.; Kohn, Robert; Weinstein, M. I.

In: Structural and Multidisciplinary Optimization, Vol. 36, No. 5, 11.2008, p. 443-456.

Research output: Contribution to journalArticle

Heider, P. ; Berebichez, D. ; Kohn, Robert ; Weinstein, M. I. / Optimization of scattering resonances. In: Structural and Multidisciplinary Optimization. 2008 ; Vol. 36, No. 5. pp. 443-456.
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