The Application of Neural Networks to the Modeling of Magnetic Hysteresis
Why this work is in the frame
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Bibliographic record
Abstract
Accurately modeling magnetic hysteresis plays a crucial role in developing precise digital twins for low-frequency electromagnetic systems. However, in large 3-D analysis systems, the evaluation of hysteresis performance at hundreds of thousands of points in components containing magnetic steels is challenging. It is of utmost importance that any modeling system can assess the hysteresis performance within the shortest possible timeframe. The utilization of neural networks (NNs) offers the potential to achieve this objective. This article provides a comprehensive review of various NN architectures that can be employed to address this requirement. Two types of learning approaches are explored: completely data-driven approaches and hybrid approaches that combine data and hysteresis laws. A comparative study is conducted to analyze the predictive power and computational cost of the different architectures under investigation.
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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 | 0.001 |
| 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