Efficient simulation of 5G Antenna platforms and Circuits using the Characteristic Basis Function Method (CBFM) and GPU Acceleration
Bibliographic record
Abstract
Modeling and simulation of electromagnetic devices, such as mobile phones, operating at millimeter wavelengths, e.g., at 30GHz, is challenging because the dimensions of the platform are typically large as compared to the operating wavelength. In this work, we propose several strategies for addressing the issue of CPU time and memory consumption, while preserving the accuracy of the computation. This include geometry simplification as well as application of a numerical algorithm for memory reduction and GPU acceleration. Memory reduction is achieved by employing an efficient iteration-free technique called the Characteristic Basis Functions Method (CBFM) proposed in [1] , to solve problems involving a large number of degrees of freedom. The CBFM has been extensively investigated by a number of authors, including [2] , to analyze objects embedded in multi-layered media.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".