Neural network EM-Field based modeling for 3d substructure in finite element method
Bibliographic record
Abstract
Most existing neural network (NN) approaches for modeling EM problems are based on training with the S-parameter at the external input-output ports of the EM structures. In this paper we present a new method to describe EM behavior at the internal interface between decomposed 3D substructures. We train NNs to represent the EM behavior of the substructure as seen from the interface. This approach allows EM coupling effect between substructures to be better represented. The method is developed in the finite element method (FEM) environment. EM transfer function matrix is formulated to produce training data to allow the NN to learn the coupling between EM field variables at various locations across the entire interface of the 3D substructure. A new formulation allowing trained NN models to be connected with FEM equations of other substructures for efficient simulation of the overall EM structure is proposed. Examples of waveguide circuit simulations show that the proposed method provides better accuracy and efficiency over the conventional neural network method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".