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Record W2959055280 · doi:10.1364/ol.44.003542

Optical generation and control of spatial Riemann waves

2019· article· en· W2959055280 on OpenAlexafffund
Domenico Bongiovanni, Benjamin Wetzel, Pengzhen Yang, Yi Hu, Yujie Qiu, Jingjun Xu, S. Wabnitz, Zhigang Chen, Roberto Morandotti

Bibliographic record

VenueOptics Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNonlinear Waves and Solitons
Canadian institutionsInstitut National de la Recherche Scientifique
FundersKey ProgrammeNatural Sciences and Engineering Research Council of Canada1000 Talents Sichuan ProgramMinistry of Education and Science of the Russian FederationNational Natural Science Foundation of ChinaMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsPhysicsInviscid flowOpticsNonlinear systemAmplitudePhase (matter)Burgers' equationShock waveShock (circulatory)Wave propagationClassical mechanicsMechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

We extend the concept of Riemann waves (RWs) to the spatial domain and demonstrate for the first time, to the best of our knowledge, Riemann beams with a propagation scenario allowing controllable shock formation in a nonlinear optical system. Similar to their standard counterparts, "shifted" RWs are characterized by a local propagation speed proportional to their local amplitude. Their steepening dynamics can be judiciously controlled by means of an additional phase term. In particular, RWs are generated by properly tailoring the initial phase of an optical beam propagating through a thermal solution of an m-cresol/nylon mixture that exhibits a giant self-defocusing nonlinearity. The experimental results show a controllable steepening and shock wave behavior, in good agreement with the prediction from the simple inviscid Burgers equation.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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