Obtaining an Operating Point Solution of a Traveling Wave Laser Model
Bibliographic record
Abstract
This paper presents a method of obtaining an operating point configuration for a laser model based on a traveling wave model (TWM), which can then be used in a circuit-level simulator. The method first finds an approximate distributed single-mode stationary solution, this solution is then iterated using the traveling wave equations to an accurate single-mode solution, and finally a short pre-simulation is used to add harmonic content to create a multi-mode configuration of the laser approximating its behavior at an operating point. The effectiveness of this approximation is tested by initiating transient simulations from this operating point and comparing them to the output of the model started from an off state. The stochastic variation in the operating point for a particular configuration is also well predicted. Included in the formulation are gain compression and dispersion effects, laser chirp due to variation in the effective index of the laser mode, and spontaneous emission. Finally, the use of the three-stage process of finding the operating point in a circuit-level simulator is discussed. Not only does the three-stage method provide a quick, accurate operating point for the circuit simulator, but the ability to provide an orders of magnitude faster estimate for the initial circuit-level operating point is critical to the practicality of its use in the simulator. The first stage of the three-stage method does just this.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".