A TWO-STAGE APPROACH FOR FREQUENCY RESPONSE MODELING AND METAMATERIAL RAPID DESIGN
Bibliographic record
Abstract
We introduce a novel two-stage approach for rapid design of massive metamaterials (MTMs), where performances of thousands of microstructures require evaluation.In Stage I, an equivalent circuit model is synthesized via rational function modeling to represent the frequency response of MTMs microstructures.In Stage II, Gaussian process (GP) regression models are unitized to build the relation between the physical setting of the microstructure, including geometric design variables and incident angles of electromagnetic (EM) waves, and the representing parameters of the equivalent circuit model.As a consequence, the mapping from the microstructure physical parameters to the frequency response is easy to achieve and with high accuracy.We offer two metamaterial prototypes to illustrate that the proposed approach allows high efficiency in facilitating the design of massive MTMs.The experimental results demonstrate that our method is no longer limited by the complexity of microstructures and the spatial dispersion, induced by the variation of incident angle.We compare the accuracy of predicted responses against the reference data, and both examples yield average RMSE less than 0.05, which meets the requirements for many MTMs engineering applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".