Applications of the partial element equivalent circuit method in computational electromagnetics simulation: An overview
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it