Artificial Magnetic Conductor Unit Cell Design Using Machine Learning Algorithms
Bibliographic record
Abstract
Commercial electromagnetic (EM) simulator tools solve complicated Maxwell’s equations to design and optimize electromagnetic devices, which is computationally expensive and time consuming. There is a dire need to solve complex electromagnetic problems with least amount of computational resources in a short time. This work proposes the application of machine learning techniques in design process of electromagnetic problem. For the proof of concept, we demonstrated an optimum design process of an artificial magnetic conductor, which is a metasurface unit cell, by applying machine learning algorithms namely, artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO). The performances of these machine learning optimization models were evaluated on the test data set based on root mean squared error (RMSE) values. To the best of our knowledge, this is the first work that yields an excellent match with the original EM results from a commercial simulator tool with very small training dataset. Thus, it obviates the need of using computationally expensive and time-consuming electromagnetic simulators and massive training datasets for data-driven design approach of complex electromagnetic problems.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".