A Neural Network based Electromagnetic Simulator
Why this work is in the frame
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Bibliographic record
Abstract
Simulating electromagnetic problems using the finite difference method or the finite element method can lead to large systems of linear equations which need to be solved. Often in the design process, while fine tuning, few system parameters are changed, while the overall system remains largely the same. The system is simulated repeatedly to find the optimal parameters, which can be a time-consuming process. In this paper we propose a new method that uses a neural network trained on a lot of variations of similar problems that can be used to get a quick estimation of the system's response to small changes in the parameters. Rather than attempting to solve the electromagnetic problem with a neural network, which has been done before, we focus on getting an extremely fast, but also accurate estimation. A concrete example problem is demonstrated through the simulations of a coaxial a cable with varying inner conductor shapes. Details about the design of the neural network regarding the choice of hyperparameters and the network's architecture are given. Additionally, an evaluation shows the performance of different proposed neural network architectures.
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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) | 0.017 | 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