Causal Parameter Extractions by Vector Fitting for Use in Time-domain Numerical Modeling
Bibliographic record
Abstract
In time-domain modeling techniques, such as the finite-difference time-domain method, a lumped parameter electronic device, such as a transistor, is often treated as a black box represented by its time-domain network parameters. The parameters of most electronic devices are, however, often given in the frequency domain and in a limited frequency range. Therefore, they need to be transformed into the corresponding time-domain parameters for inclusion in time-domain modeling. The vector fitting technique is a robust coefficient extraction technique that circumvents the normal ill-conditioning and unbalanced weighting problems occurring in a rational approximation or fitting process. We apply it to obtain frequency domain rational approximation functions of network parameters of a lumped parameter device and then convert them to the corresponding time-domain parameters. As a result, the time-domain parameters are not only causal but also exponential in time. Convolution can then be performed in a recursive fashion without the need to involve a complete past history of the time-domain data. In a long simulation, the CPU time saving factor can be hundreds and thousands of times.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".