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June 3, 2026Journal of Electromagnetic Engineering and ScienceOpen Access

Designing Multilayered Electromagnetic Absorbers with Machine Learning

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Authors

BRBiswarup RanaIHIc-Pyo Hong

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Overview

Generative model predicts graphene configurations in absorbers, suggesting a new fabrication approach.

Key Points

  • This work aims to create a machine learning model that predicts configurations for multilayer electromagnetic absorbers.
  • Utilized a conditional variational autoencoder with a residual network for feature extraction.
  • Prepared the dataset using High Frequency Structure Simulator for electromagnetic simulation.
  • Predicted the upper graphene structure using S-parameters and the lower graphene structure.
  • Achieved satisfactory agreement between predicted and actual graphene structures.
  • Demonstrated improved performance and efficiency in multilayer absorber design.

Cite This Study

Rana et al. (2026) studied this question.

synapsesocial.com/papers/6a1fc696dee9eb8c0dce78ddhttps://doi.org/10.26866/jees.2026.3.r.359
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