Implementation of Iron Loss Model on Graphic Processing Units
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
Design engineers are always looking for extra computational power to speed up the execution of their tasks. One way to achieve this speedup is to identify tasks with a high degree of parallelism and process them with graphic processing units (GPUs). GPUs are optimized to process such tasks efficiently and quickly in massive multicore hardware. The steps involved in a finite-element (FE) electromagnetic simulation are computationally very expensive. One such step is the communication between FE solver and the material loss model that takes place for all the elements in the mesh for each time step. This task is massively parallel and, thus, could be executed in a GPU. As an example, a physics-based material model, the Jiles-Atherton model, is implemented in a GPU to compute the B-H hysteretic relationship, which can be directly incorporated in FE simulations. The performance of the GPU is compared with that of the given microprocessor in terms of computational time. A time gain of 13.8 times has been achieved.
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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