Inverse design of ultra-wideband transparent frequency selective surface absorbers based on evolutionary deep learning
Bibliographic record
Abstract
Abstract Conventional frequency selective surface (FSS) absorbers design is time-consuming, involving multiple electromagnetic (EM) simulations for parameter scanning. A novel reverse design method is proposed utilizing evolutionary deep learning (EDL) based on an improved bacterial foraging optimization (IBFO) algorithm and a deep belief network. It establishes the relationship between the geometric structure and EM response. The combination of IBFO and EDL facilitates an efficient optimization for structural parameters, mitigating the ‘one-to-many’ problem and accelerating the design process. An optically transparent FSS absorber with an ultra-bandwidth of 8–18 GHz is designed to verify the proposed method’s capability. The simulation and experimental results demonstrate that the absorber displays exceptional characteristics such as polarization insensitivity and robustness under a 45° oblique incidence angle, making it a suitable candidate for radar stealth and photovoltaic solar energy applications. The proposed method can be applied to the design and optimization of various absorbers and complex EM devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".