Parallel Automatic Model Generation Technique for Microwave Modeling
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a parallel automatic model generation (PAMG) technique is proposed to speedup the development of artificial neural network (ANN) models for microwave modeling. The automatic model generation (AMG) converts human based manual modeling into an automated computational process. AMG typically involves intensive computations in adaptive data sampling by repetitively driving detailed EM/physics/circuit simulators, and automatic ANN structure adaptation through iterative training stages. To improve AMG efficiency, a parallel mechanism is developed, in which the computationally intensive processes are split into smaller sections. These sections are concurrently executed on parallel processors in a multi-processor environment. The proposed parallel algorithm is formulated to maximize the number of parallel processes while minimizing the sequential overhead in the AMG to achieve the highest possible modeling efficiency. Examples of driving a physics-based device simulator for MESFET modeling and driving a circuit simulator for power amplifier behavior modeling demonstrate that the proposed PAMG dramatically shortens the model development time with parallel efficiency above 90%, thus is very useful for large-scale microwave modeling.
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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) | 0.000 | 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 | 0.000 |
| 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