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 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) | 0.000 | 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 teacher head, 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".