Automated Neural Network-Based Multiphysics Parametric Modeling of Microwave Components
Bibliographic record
Abstract
This paper proposes a novel technique for automated neural network based multiphysics parametric modeling of microwave components. For the first time, we propose to utilize automated model generation (AMG) algorithm in the field of electromagnetic (EM) centric multiphysics parametric model development to improve the neural-based multiphysics modeling efficiency. All the subtasks in developing a neural network based multiphysics parametric model, including EM centric multiphysics data generation, neural network structure adaptation, training and testing, are integrated into one unified and automated framework, thus converting the conventional human-based manual modeling into an automated computational process. In the proposed algorithm, automated EM centric multiphysics data generation is realized by automatic driving of multiphysics simulation tools. Parallel computation technique is incorporated to further speedup the data generation process by driving multiple EM centric multiphysics simulations on parallel computers simultaneously. In addition, automated neural model structure adaptation algorithm for multiphysics parametric modeling is also proposed. In this way, the proposed technique automates the neural-based multiphysics model development process and significantly reduces the intensive human effort and modeling time demanded by the conventional manual multiphysics modeling methods. The achieved neural model can be used to provide accurate and fast prediction of the EM centric multiphysics responses of microwave components in high-level multiphysics design. Examples of multiphysics parametric modeling of two microwave filters are presented to show the advantage of this work.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".