Benchmarking Physics-Informed Neural Networks for Time-Domain Electromagnetic Simulations
Why this work is in the frame
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Bibliographic record
Abstract
We benchmark physics-informed neural networks (PINNs) for time-domain electromagnetic simulations, systematically addressing fundamental questions on their accuracy, stability and computational efficiency. A U-Net is trained to solve the wave equation for the electric field in the time domain, in an unsupervised manner. The performance of the physics-informed U-Net is evaluated by varying its architecture, while monitoring its accuracy and stability. Thus, we propose a counterpart of the familiar and insightful dispersion and stability analysis of conventional numerical techniques (such as FDTD) for PINNs. We demonstrate the significance of this approach for building effective and accurate PINNs for time-domain electromagnetic simulations.
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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) | 0.001 | 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