Stochastic Modeling of Wave Propagation in Waveguides With Rough Surface Walls
Bibliographic record
Abstract
Waveguide components at terahertz (THz) frequencies suffer from increased conductor losses. These losses are further exacerbated by surface roughness. In this article, we introduce an expedient approach for modeling surface roughness, which is suitable for microwave to THz applications. Our model combines the high accuracy of a full-wave method with the computational efficiency of the polynomial chaos expansion (PCE). Wave propagation inside the waveguide is simulated with the finite-difference time-domain (FDTD) method, capturing both the excess attenuation and diffuse scattering phenomena due to roughness. Then, a small number of FDTD simulations (considerably smaller than what is needed in the standard Monte Carlo procedure) is used to determine the coefficients for the PCE representation of the guided waves as random variable functions. This allows us to determine the probability density function of all electromagnetic field components in a rough surface waveguide. Extraction of mean value and standard deviation of fields, as well as of other quantities of interest such as the attenuation constant, is just a matter of rapid postprocessing of these results over the entire simulated bandwidth.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".