Solving design and inverse-imaging problems through electromagnetic simulation
Bibliographic record
Abstract
Numerical electromagnetic analysis has been rapidly developing for more than three decades now, matching closely the remarkable progress of computing technology. Commercial packages for high-frequency computer-aided analysis are nowadays standard toolboxes in industrial and academic microwave laboratories. Yet designers and researchers rarely use full-wave simulations in the early to intermediate stages of the design process. The usual practice is to use them only as a final verification tool before prototyping. This is not for the lack of suitable optimization algorithms as these have grown to no lesser degree of sophistication and commercialization than electromagnetic solvers. The weakest link in electromagnetic computer-aided design is that between the simulation and the optimization algorithms. In this talk, we review the latest developments in simulation-based optimization in microwave engineering. We outline the requirements of optimization algorithms in design and inverse problems, and we discuss how electromagnetic simulators must improve to meet these demands. We illustrate the importance of these developments through design and microwave-imaging examples.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".