From Noise to Pattern: Inverse Design of FSS Using Variational Autoencoder,

dc.contributor.authorBoudagh, Francisco
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysiksv
dc.contributor.departmentChalmers University of Technology / Department of Physicsen
dc.contributor.examinerVolpe, Giovanni
dc.contributor.supervisorGouda, Ahmed
dc.date.accessioned2025-06-16T06:52:30Z
dc.date.issued2025
dc.date.submitted
dc.description.abstractFrequency Selective Surfaces (FSSs) are critical for modern electromagnetic filtering, but their design traditionally relies on costly trial-and-error simulations. We present a generative machine learning framework for the inverse design of FSS unit cell patterns. By utilizing a conditional variational autoencoder (cVAE), the proposed method directly maps desired electromagnetic scattering parameters (S-parameters) to FSS patterns, thereby circumventing the traditional energy- and time-expensive trial-and-error approach inherent in FSS design. A dataset of 10,000 simulated (pat tern, S-parameter) samples was generated using Ansys HFSS over the 2 to 8 GHz frequency range to train both a surrogate neural network, which accurately predicts the S-parameters from a given pattern, and a cVAE-based generator that synthe sizes novel pattern designs conditioned on target frequency responses. The integrated framework employs a gradient-based optimization strategy in the la tent space to minimize the deviation between the predicted and desired S-parameter responses, with particular emphasis on preserving the resonant frequency. Bench marking on the test dataset demonstrates that the surrogate model achieves mean absolute errors from 0.5 dB at 2 GHz to 1.9 dB at 8 GHz, while the optimization loop refines designs to yield deviations as low as 0.0-0.2 GHz at the resonant fre quency for half of the samples. These results underscore the promising potential of generative machine learning for rapid FSS inverse design.
dc.identifier.coursecodeTIFX05
dc.identifier.urihttp://hdl.handle.net/20.500.12380/309440
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectMachine learning, variational autoencoder, artificial neural networks, inverse problem, optimization, frequency selective surfaces, metamaterials
dc.titleFrom Noise to Pattern: Inverse Design of FSS Using Variational Autoencoder,
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc

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