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 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".