Recent advances in neural based time domain EM modeling and simulation
Bibliographic record
Abstract
In this paper, the recent neural network (NN) approaches to time domain electromagnetic (EM)-based modeling are summarized. Fast and accurate passive EM models can be created using three recent methods, i.e., (equivalent circuit and neural network) EC-NN, (state space equation and neural network) SSE-NN and (equivalent circuit, state space equation and neural network) EC-SSE-NN. Those methods are based on combined equivalent circuit and/or state space theory. Each of the combined modeling techniques has its own usage depending on the availability of the equivalent circuit and user-desired accuracy. In order to develop a nonlinear transient model to be used together with passive components in time domain EM-based simulation, the adjoint dynamic neural network (ADNN)-based modeling technique can be utilized. Through accurate and fast time domain EM-based neural models of passive/active components, we enable consideration of EM effects in high-frequency and high-speed computer-aided design (CAD), including component's geometrical/physical parameters as optimization variables. Examples of EM modeling of embedded passives and their use in time domain EM-based simulation and design are presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".