“Super-efficient gradient estimation technique,” Recent advances in efficient adjoint sensitivity analysis and its application in metamaterial design
Bibliographic record
Abstract
Computer-aided design (CAD) tools in electromagnetics allow accurate modeling of the preferred responses. We can adjust a desired response by conducting an optimization algorithm. This requisite the gradient estimation of structures with respect to potentially N number of optimizable parameters. In conventional gradient estimation methods, the number of required simulations scales linearly with N. Adjoint variable method (AVM) is an extremely efficient sensitivity analysis method that estimates the gradients with respect to the all N parameters by conducting only 2 simulations, regardless of N. We have developed AVM method for gradient analysis of anisotropic and dispersive anisotropic structures. Then, we applied it in wideband inversely invisibility cloak design for 2-D and 3-D structures at optical and microwave frequency regions. In those examples, our algorithm accelerates the gradient estimation over 1400 and 12 500 times per iteration, respectively, compared to the conventional methods. This advanced optimization based metamaterial cloak design to 3-D arbitrary shape objects and optical frequency region which was not feasible before. For the next step, we are planning to extend this efficient gradient estimation algorithm to the state of the art machine learning techniques and recently introduced continuous neural networks such as neural ordinary differential equations (ODE-Net).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".