Physics-Guided CNN Architecture Design for Irregular Terrain Propagation Modeling
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".