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Record W2765749019 · doi:10.1073/pnas.1719346115

Geometric hydrodynamics via Madelung transform

2018· article· en· W2765749019 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldMathematics
TopicGeometric Analysis and Curvature Flows
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Colorado BoulderWeizmann Institute of ScienceStiftelsen för Strategisk Forskning
KeywordsMetric (unit)Partial differential equationSymplectomorphismPhase spaceSpace (punctuation)Mathematical analysisDifferential geometryMathematicsPhysicsClassical mechanicsSymplectic geometryComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

Significance Geometry has always played a fundamental role in theoretical physics via symmetries and conservation laws. We present a geometric framework revealing a closer link between hydrodynamics and quantum mechanics than previously recognized. Newton’s equations, generalized to infinite-dimensional spaces of fluid flow maps (diffeomorphisms), are used to develop a unified setting and uncover new connections between many equations of mathematical physics. These include equations of compressible fluids, motion of particles on spheres in quadratic potentials, and the Klein–Gordon and nonlinear Schrödinger equations, as well as their relation to information geometry and optimal mass transport. This work contributes toward a better understanding of geometric structures arising in hydrodynamics and quantum mechanics.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.047
GPT teacher head0.321
Teacher spread0.274 · 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