Advanced Simulation-Inserted Optimization Using Combined Quasi-Newton Method With Lagrangian Method for EM-Based Design Optimization
Bibliographic record
Abstract
Traditional electromagnetic (EM)-based design optimizations typically treat the EM simulation as a black-box and use optimization algorithms to externally drive EM simulations iteratively. Recently, a novel EM-based design optimization technique, named as simulation-inserted optimization (SIO) in this article, has been developed by opening the black-box and inserting the optimization algorithm into the internal of EM simulation to achieve EM field solutions and the optimal values of design variables simultaneously. This article proposes an advanced SIO algorithm using combined quasi-Newton method with Lagrangian method. In the proposed algorithm, the finite element method (FEM) is used as the EM simulation method, and the Lagrangian method is incorporated to internally integrate EM simulation with design optimization. New formulations based on quasi-Newton method are derived to approximate the computationally intensive lower upper (LU) decomposition of the FEM system matrix in the current optimization iteration by that in the previous optimization iteration to significantly reduce the number of LU decompositions. The proposed SIO technique further derives novel optimization update formulations by introducing the line search algorithm to increase the robustness of optimization. Using the proposed SIO algorithm, only a few initial LU decompositions of FEM system matrices need to be calculated instead of repetitively calculating a large number of LU decompositions of FEM system matrices during optimization, consequently speeding up the overall optimization process. The proposed technique is demonstrated by two application examples of EM-based design optimization of microwave components.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".