Physics-Guided CNN Architecture Design for Irregular Terrain Propagation Modeling
Why this work is in the frame
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Bibliographic record
Abstract
The two-way split-step parabolic-equation (SSPE) method has been extensively employed for modeling radio-wave propagation over irregular terrain. Despite its accuracy, the SSPE method incurs substantial computational cost when applied to electrically large scenarios. To mitigate this burden, recent research has explored the use of machine learning (ML) models. However, the general-purpose networks adopted in these studies often fail to account for underlying electromagnetic principles, resulting in limited generalization, especially with respect to antenna parameters. To address this limitation, we propose a convolutional neural network (CNN) architecture that embeds electromagnetic priors into its design. A physics-guided parameter embedding block, inspired by the computational procedure of the SSPE algorithm, is introduced to enhance the model’s ability to generalize across diverse antenna characteristics. Informed by the multipath propagation characteristics inherent to terrain environments, we design a nested U-shaped network structure to enhance the model’s feature representation capacity. We demonstrate the efficacy of the proposed framework through numerical experiments performed across a wide range of terrain profiles and antenna configurations. Additional validation using measured data over real terrain scenarios further confirms the applicability of the model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it