Efficient Reduced-Order Electromagnetic Optimization via Augmented Lagrangian and Newton Method
Bibliographic record
Abstract
Traditional electromagnetic (EM) design optimization often relies on high-fidelity simulations and external optimization algorithms, resulting in excessive runtime and substantial memory consumption. Simulation-inserted optimization (SIO) methods, such as complex Newton’s SIO (CNSIO), integrate optimization directly within the EM simulation loop, alleviating the computational and memory overhead of conventional closed-box approaches. However, CNSIO still requires full-order EM model evaluations at each iteration, which limits its scalability for large-scale, multiparameter problems. This article proposes the reduced-structure augmented Lagrangian with complex Newton (RSAL-CN) method, which integrates model order reduction (MOR) that preserves the essential EM characteristics, augmented Lagrangian method (ALM), and CNSIO. The second-order Arnoldi method for passive order reduction (SAPOR) is used for model reduction, while ALM internally couples EM simulation with constraint handling. New formulations based on CNSIO avoid repeated Hessian factorizations and accelerate convergence. Optimization is performed directly on reduced-order EM models, significantly reducing runtime and memory usage while maintaining accuracy comparable to CNSIO. Validation on two waveguide filter design examples demonstrates that RSAL-CN is more efficient and robust than CNSIO, offering a practical solution for large-scale EM design optimization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".