Inverse design of optical pulse shapes for time-varying photonics
Bibliographic record
Abstract
There has been an explosion of interest in time-varying photonics due to the recent discovery and design of materials and metamaterials with strong, time-varying, nonlinear optical responses. This opens the door to novel optical phenomena including reciprocity breaking, frequency translation, and amplification that can be enhanced by optimizing the light-matter interaction. Although there has been recent interest in applying topology-based inverse design to this problem, we have decided to take a more novel approach. In this article, we will introduce a method for the inverse design of optical pulse shapes to enhance their interaction with time-varying media. We test our objective-first approach by maximizing the transmittance of optical pulses of equal intensity through time-varying media. Indeed, without requiring a change in pulse energy, we demonstrate large, broadband enhancements in the pulse energy transmission through the thin-films, including gain. Our final test includes maximizing pulse transmission through indium tin oxide, a time-varying medium when strongly pumped in its ENZ band. Through this work, we hope to inspire exploration of this new degree of freedom.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".