Multifidelity Space-Mapping-Based Approach for Accelerated Multiphysics Optimization of Microwave Devices
Bibliographic record
Abstract
This article proposes an advanced multifidelity space-mapping-based approach to accelerate the multiphysics optimization of microwave devices. The proposed method develops a novel two-layer space mapping (SM) framework that strategically integrates two complementary mappings. The first-layer mapping bridges high-fidelity multiphysics analysis (multiphysics fine model) with single-physics electromagnetic (EM) analysis (EM fine model), while the second-layer mapping further leverages this EM fine model to a computationally inexpensive EM coarse model constructed on a coarse mesh (EM coarse model). To effectively capture the complex nonlinear relationships between different fidelity models, artificial neural networks (ANNs) are employed to construct both mappings, enabling efficient fidelity transitions and preserving surrogate model accuracy. A trust-region optimization algorithm, tailored for this multifidelity SM framework, is developed to guarantee stability and convergence efficiency during the optimization process. This algorithm updates the optimization step size based on the local credibility of the surrogate model, effectively guiding the design toward a robust convergence with limited high-fidelity data. By utilizing the two-layer neural network mapping and the custom optimization strategy, our method systematically shifts computationally intensive optimization tasks to the lowest-fidelity model. This significantly reduces the reliance on expensive multiphysics simulations. Validation through three diverse design examples, including waveguide filters and a tunable antenna, demonstrates that the proposed method excels in accelerating multiphysics optimization. The framework effectively reduces the reliance on expensive multiphysics simulations and shortens the total optimization time compared to conventional surrogate modeling approaches, providing a powerful tool for the efficient design of complex microwave devices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".