Neural-Network Modeling for 3-D Substructures Based on Spatial EM-Field Coupling in Finite-Element Method
Bibliographic record
Abstract
This paper presents a new neural-network method to describe the electromagnetic (EM) behavior at the interface between the substructures from an internally decomposed EM structure. A set of neural networks is used to represent the EM behavior of the substructure as seen from the interface. This allows EM coupling between substructures to be effectively represented. The method is developed in a finite-element environment. An EM transfer function matrix is formulated to produce training data, allowing neural networks to learn the spatial coupling between EM-field variables at various locations over the interface of the substructure. A new formulation is proposed where trained neural networks are integrated into the finite-element equation for efficient simulation of an overall EM structure. A technique is developed to allow the proposed model to be used with the mesh different from that in neural-network training. Examples show that the proposed method provides better accuracy than conventional neural-network approaches for modeling substructures from an internally decomposed EM problem. Using the proposed model also speeds up finite-element simulation.
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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) | 0.001 | 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 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".