A Parallel Finite-Element Time-Domain Method for Nonlinear Dispersive Media
Why this work is in the frame
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Bibliographic record
Abstract
In this article, a novel use of graphics processing units (GPUs) is presented for the acceleration of finite-element time-domain (FETD) methods containing electrically complex media. By leveraging the massively parallel architecture of the GPU via NVIDIA's Compute Unified Device Architecture (CUDA) language, the immense computational burden imposed by these materials can be largely alleviated, facilitating their modeling and incorporation into electromagnetic devices and systems. To that end, an analysis of both mixed and vector wave equation-based nonlinear dispersive FETD algorithms is presented in order to both identify computational bottlenecks and determine their amenability to parallelization. Based on this analysis, a parallel elemental matrix-evaluation procedure is proposed, which when coupled to the recently derived Gaussian belief propagation method for matrix assembly and solution, demonstrates a performance increase of up to 200 times as compared with a traditionally serial implementation.
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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 | 0.000 | 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) | 0.001 | 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