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Record W4385403800 · doi:10.1016/j.simpa.2023.100557

Hyper2D: A finite-volume solver for hyperbolic equations and non-equilibrium flows

2023· article· en· W4385403800 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

VenueSoftware Impacts · 2023
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaPolitecnico di MilanoUniversity of OttawaNvidia
KeywordsFortranSolverPartial differential equationFinite volume methodApplied mathematicsHyperbolic partial differential equationComputer scienceOrdinary differential equationCUDAComputational scienceMathematicsDifferential equationPhysicsMathematical optimizationMathematical analysisMechanicsParallel computing

Abstract

fetched live from OpenAlex

Hyper2D is a finite-volume solver for hyperbolic partial differential equations (PDEs) and non-equilibrium flows. Its minimalistic structure makes Hyper2D quickly adaptable to one’s needs. Non-standard systems of equations and source terms are easily implemented by modifying a single pde file. In our research, we use Hyper2D for studying rarefied hypersonic gas dynamic problems (moment methods), relativistic flows, multi-fluid plasma models and kinetic theory (1D1V Boltzmann/BGK equation). The package includes (i) a one-dimension Octave/MATLAB version, aimed at familiarizing with the method, (ii) a single-core Fortran version, with higher-order accuracy in space and time, and (iii) a CUDA Fortran version.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.687

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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