SAS-PINN: An Enhanced Physics-Informed Neural Network for 2-D Time-Domain Electromagnetic Field Computation of Power Transformer
Bibliographic record
Abstract
In this paper, an enhanced physics-informed neural network (PINN) framework is proposed for accurate time-domain electromagnetic field computation in power transformers. To address the numerical stiffness and convergence challenges arising from steep field gradients between ferromagnetic cores, dielectric materials, and multi-layer windings, the high-frequency representation capability of the SIREN network architecture is leveraged. A novel adaptive collocation point sampling strategy is developed to dynamically refine spatial-temporal sampling resolution in high-gradient regions, effectively balancing numerical accuracy with computational efficiency. The proposed framework rigorously embeds Maxwell’s equations and composite boundary conditions into the loss formulation, establishing a surrogate model for 2D electromagnetic field computation. Numerical results demonstrate a two-order-of-magnitude reduction in prediction error compared to vanilla PINNs with random sampling strategy. This breakthrough enables efficient simulation of time-domain multi-physics fields in complex electromagnetic devices featuring intricate geometries and multi-material interfaces.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".