Applications of the partial element equivalent circuit method in computational electromagnetics simulation: An overview
Bibliographic record
Abstract
Computational electromagnetics (CEM) simulation is employed in diverse applications to analyze electromagnetic (EM) fields and waves, providing critical insights essential for design and optimization. Among the various available CEM techniques, such as the finite element method (FEM) and the finite-difference time-domain (FDTD) method, the partial element equivalent circuit (PEEC) method is an upcoming and preferred technique in certain applications due to its capability to integrate EM and circuit simulations, as well as its reduced computational cost. Consequently, publications based on the PEEC method have exhibited an increasing trend in the recent years. This study provides a comprehensive overview of PEEC-based applications across various fields to address prevailing problems in these applications. The survey categorizes the publications based on the type of application and provides detailed information on the techniques used and the results obtained. This work also briefly highlights the growing trend of adopting model order reduction (MOR) techniques, emphasizing their compatibility with the partial element equivalent circuit (PEEC) method to achieve more efficient and effective solutions. This review paper is valuable for researchers and engineers in related fields, aiding them in pinpointing future research opportunities and effectively employing the PEEC technique.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".