In EDS ansehen

Expedited Gradient-Based Design Closure of Antennas Using Variable-Resolution Simulations and Sparse Sensitivity Updates.

Gespeichert in:
Bibliographische Detailangaben
Titel: Expedited Gradient-Based Design Closure of Antennas Using Variable-Resolution Simulations and Sparse Sensitivity Updates.
Autoren: Pietrenko-Dabrowska, Anna, Koziel, Slawomir
Quelle: IEEE Transactions on Antennas & Propagation; Jun2022, Vol. 70 Issue 6, p4925-4930, 6p
Schlagwörter: ANTENNA design, BROADBAND antennas, GLOBAL optimization, APPROXIMATION algorithms
Abstract: Numerical optimization has been playing an increasingly important role in the design of contemporary antenna systems. Due to the shortage of design-ready theoretical models, optimization is mainly based on electromagnetic (EM) analysis, which tends to be costly. Numerous techniques have evolved to abate this cost, including surrogate-assisted frameworks for global optimization, or sparse sensitivity updates for speeding up local search. In the latter, CPU-heavy updates of the system response sensitivity through finite differentiation are suppressed based on, e.g., the magnitude of design variability during the optimization run. Another approach is to incorporate variable-resolution simulations. Recently, a technique exploiting a continuous spectrum of admissible model fidelity levels has been reported, thereby allowing for a considerable reduction of computational expenditures. Seeking further savings, this work introduces an accelerated gradient-based algorithm with sparse sensitivity updates and variable-resolution EM simulations. Our technique is validated using four broadband antennas, and demonstrated to offer substantial (around 80%) savings over the benchmark while maintaining acceptable design quality. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Antennas & Propagation is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract:Numerical optimization has been playing an increasingly important role in the design of contemporary antenna systems. Due to the shortage of design-ready theoretical models, optimization is mainly based on electromagnetic (EM) analysis, which tends to be costly. Numerous techniques have evolved to abate this cost, including surrogate-assisted frameworks for global optimization, or sparse sensitivity updates for speeding up local search. In the latter, CPU-heavy updates of the system response sensitivity through finite differentiation are suppressed based on, e.g., the magnitude of design variability during the optimization run. Another approach is to incorporate variable-resolution simulations. Recently, a technique exploiting a continuous spectrum of admissible model fidelity levels has been reported, thereby allowing for a considerable reduction of computational expenditures. Seeking further savings, this work introduces an accelerated gradient-based algorithm with sparse sensitivity updates and variable-resolution EM simulations. Our technique is validated using four broadband antennas, and demonstrated to offer substantial (around 80%) savings over the benchmark while maintaining acceptable design quality. [ABSTRACT FROM AUTHOR]
ISSN:0018926X
DOI:10.1109/TAP.2021.3138487