Physics-Based Modeling for Hybrid Data-Driven Models to Estimate SNR in WDM Systems
Why this work is in the frame
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Bibliographic record
Abstract
Recently several machine learning methods have been proposed to estimate the SNR, based on launch data and other system factors. These data-driven methods typically require a large number of datasets for training and generally are not interpretable. In this paper, we propose an alternative approach that requires less data and is interpretable, specifically a hybrid algorithm combining a physical model with Gaussian process regression. We develop a measurement-informed physical model, systematically reducing the number of independent parameters based on the underpinning physics and improve the overall performance of the physical model marginally. The model is validated using measurements performed on a 15-channel wavelength-division multiplexed system propagating over 1,000 km of standard single-mode fiber. The proposed hybrid model is not only interpretable but also obtains better agreement with measurements than a Gaussian process regression model and a simple neural network model for a given number of training datapoints.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it