Design Optimization of Pulse Transformers in Series-Type Hybrid Circuit Breakers Using a Neural Network Based Surrogate Model
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Bibliographic record
Abstract
Multi-objective optimization is often required in designing electromagnetic components, such as motors or transformers, under complicated trade-off considerations. Finite Element Method (FEM) simulations are usually conducted to achieve the optimal design with the penalty of long computation time. This paper presents a data-driven design optimization method that partially replaces FEM simulation with a deep neural network (DNN) based surrogate model. The proposed framework adopts the Non-dominated Sorting Genetic Algorithm II (NSGAII) method. Through the case study of a pulse transformer design in a series-type hybrid circuit breaker (S-HCB), we demonstrate that the new DNN- based optimization approach can significantly reduce the optimization time by sevenfold while preserving up to 98.66% of the average accuracy of the conventional FEM approach. The proposed optimization strategy is expected to benefit a wide range of engineering optimization problems that involve a large amount of computation.
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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