Machine learning and artificial neural networks for improved algorithmic design of nanophotonic structures
Bibliographic record
Abstract
The study of the behavior of light at the nanometer scale is known as nanophotonics and it is the field that combines nanotechnology along with photonics. Over the past two decades, nanophotonics has been a promising and active research area, sparked by the growing interest in discovering new physics and technologies with light at the nanoscale. As the requirements on the level of integration and performance increase of nanophotonic applications, the design and optimization of nanostructures, with an immense number of possible combinations of features, for nanophotonic devices become time-inefficient and computationally expensive with the numerical simulations. The recent theoretical results show that machine learning (ML) and artificial neural network (ANN) techniques are capable of model nanophotonic structures for nanophotonic devices, at orders of magnitude lower time per result. It was a paradigm shift of research in nanophotonics to use ANNs as it has the most important advantages to use over existing traditional methods. Therefore, this project suggests utilizing ANN techniques to improve the algorithmic design of nanophotonic structures.
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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) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".