Time-Domain Computational Electromagnetics Algorithms for GPU Based Computers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time-domain computational electromagnetic algorithms such as FDTD and TLM require computers with superb processing power and large memory capacity. Grid computing network, cluster computer and massively parallel supercomputers have been the hardware of choices for running powerful modelling tools based on these methods. As a result, high performance modelling tools are only available to elite groups of researchers and big corporations. Stream computing, a new technology that harnesses the tremendous numerical processing power of advanced graphics processing units for general purpose numerical computation, is going to bring high performance time-domain modelling tools to the EM community. This paper reviews the emerging GPU technologies and programming models. Two modelling examples are also used to illustrate the suitability of GPU computing for time-domain electromagnetics.
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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) | 0.000 | 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