Modeling and simulation of photonic devices by generalized space mapping technique
Bibliographic record
Abstract
The generalized space mapping (GSM) technique is employed for modeling and simulation of photonic devices. The mapping is established between the parameter spaces of coarse and fine models so that accuracy of the coarse model is significantly improved for a given range of parameters. To demonstrate the usefulness of this technique, modeling and simulation of an optical waveguide facet is used as an application example. Two methods are adopted for the coarse model, i.e., the transfer matrix method (TMM) and the free space radiation mode (FSRM) method. The time-intensive and accurate finite-difference time-domain (FDTD) method is used as the fine model for model calibration. The mapping-enhanced coarse models show significant improvement in terms of accuracy. Further, a criterion is established to measure the accuracy of the coarse models. It is shown that the FSRM is one order of magnitude more accurate than the TMM for the TE case, however, the two methods have almost the same level of accuracy for the TM case.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".