A Generalizable Physics-Guided Convolutional Neural Network for Irregular Terrain Propagation
Bibliographic record
Abstract
The application of split-step parabolic equation (SSPE) methods for radio wave propagation across irregular terrains has gained widespread attention. However, the computational intensity of these methods limits their practical use, leading to the exploration of machine learning (ML) techniques as an alternative. A significant hurdle for ML models in the field of electromagnetics is their ability to precisely forecast relevant quantities in situations not covered by their training data, which have not been considered in the current ML-assisted propagation models over irregular terrain. To that end, we propose a generalizable physics-guided propagation modeling framework of high fidelity. This framework is adept at generalizing across various terrain types and antenna configurations, showcasing extrapolation capabilities beyond its training dataset. Our approach innovates by embedding prior knowledge from deterministic models into the network architecture. Furthermore, we demonstrate that adapting the network structure to align with the electromagnetic properties of terrain propagation markedly improves the model’s predictive accuracy and generalizability.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".