—An innovative System-by-Design (SbD) method is proposed for the design of non-parametric electromagnetic skin (EMS) meta-atoms. The developed technique leverages a smart encoding of the unknowns allowing to exploit physics knowledge and to enable a favorable environment for the optimizationdriven solution of the arising synthesis problem. This latter is efficiently solved by means of a customized SbD implementation relying on a Deep-Learning (DL)-based Surrogate Model (SM) of the pixel-based reflective meta-atom and the exploration of the solution space by means of an Integer-Coding Genetic Algorithm (ICGA) strategy. A preliminary proof-of-concept is shown to assess the capabilities of the proposed method.

Deep Learning Assisted Design of EM Skin Meta-Atoms within the System-by-Design / Albi, Federico; Salucci, Marco; Lin, Zhichao; Oliveri, Giacomo; Massa, Andrea. - STAMPA. - (2024), pp. 253-254. (Intervento presentato al convegno 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 tenutosi a Firenze, Italy nel 14th July - 19th July 2024) [10.1109/ap-s/inc-usnc-ursi52054.2024.10686574].

Deep Learning Assisted Design of EM Skin Meta-Atoms within the System-by-Design

Albi, Federico;Salucci, Marco;Oliveri, Giacomo;Massa, Andrea
2024-01-01

Abstract

—An innovative System-by-Design (SbD) method is proposed for the design of non-parametric electromagnetic skin (EMS) meta-atoms. The developed technique leverages a smart encoding of the unknowns allowing to exploit physics knowledge and to enable a favorable environment for the optimizationdriven solution of the arising synthesis problem. This latter is efficiently solved by means of a customized SbD implementation relying on a Deep-Learning (DL)-based Surrogate Model (SM) of the pixel-based reflective meta-atom and the exploration of the solution space by means of an Integer-Coding Genetic Algorithm (ICGA) strategy. A preliminary proof-of-concept is shown to assess the capabilities of the proposed method.
2024
2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI)
Piscataway, NJ
Institute of Electrical and Electronics Engineers Inc.
9798350369908
Albi, Federico; Salucci, Marco; Lin, Zhichao; Oliveri, Giacomo; Massa, Andrea
Deep Learning Assisted Design of EM Skin Meta-Atoms within the System-by-Design / Albi, Federico; Salucci, Marco; Lin, Zhichao; Oliveri, Giacomo; Massa, Andrea. - STAMPA. - (2024), pp. 253-254. (Intervento presentato al convegno 2024 IEEE International Symposium on Antennas and Propagation and INC/USNCURSI Radio Science Meeting, AP-S/INC-USNC-URSI 2024 tenutosi a Firenze, Italy nel 14th July - 19th July 2024) [10.1109/ap-s/inc-usnc-ursi52054.2024.10686574].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/432952
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