Inverse Design of Photonic Systems
Bibliographic record
Abstract
Abstract Inverse design methods use optimization and learning algorithms to pair desired functionalities with the corresponding high‐performing systems. Such methods have significant potential for discovering novel photonics solutions, with inverse design techniques already mediating significant milestones in nanophotonics, quantum optics, and lens systems. However, while computational tools for identifying optimal system parameters (i.e., component settings) have reached significant maturity, the identification of suitable system topologies (i.e., component choice and arrangement) has remained challenging, especially for the design of complex photonic schemes. Here, a framework for the inverse design of practical photonic systems is presented, capable of efficiently and automatically searching for high‐performance topologies and their associated operational parameters. It is demonstrated that the approach can aid in the discovery of practical photonic systems, that are both physically feasible and non‐trivial, by leveraging system‐level automatic differentiation and discrete topological changes. The versatility of the platform is supported with example designs for waveform generation, noise suppression, and sensing, among others.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".