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Record W4367318854 · doi:10.1364/oe.485902

Inverse design of optical pulse shapes for time-varying photonics

2023· article· en· W4367318854 on OpenAlexafffund
Joshua Baxter, Lora Ramunno

Bibliographic record

VenueOptics Express · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsOpticsPhotonicsComputer scienceTransmittanceBroadbandTransmission (telecommunications)MetamaterialOptoelectronicsMaterials sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

Recent advancements in materials and metamaterials with strong, time-varying, nonlinear optical responses have spurred a surge of interest in time-varying photonics. This opens the door to novel optical phenomena including reciprocity breaking, frequency translation, and amplification that can be further optimized by improving the light-matter interaction. Although there has been recent interest in applying topology-based inverse design to this problem, we propose a novel approach in this article. We introduce a method for the inverse design of optical pulse shapes to enhance their interaction with time-varying media. We validate our objective-first approach by maximizing the transmittance of optical pulses of equal intensity through time-varying media. Through this approach, we achieve large, broadband enhancements in pulse energy transmission, including gain, without altering the incident pulse energy. As a final test, we maximize pulse transmission through thin films of indium tin oxide, a time-varying medium when strongly pumped in its ENZ band. Our work presents a new degree of freedom for the exploration, application, and design of time-varying systems and we hope it inspires further research in this direction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.294
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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