MétaCan
Menu
Back to cohort
Record W2170437529 · doi:10.1145/2487228.2487230

Eulerian-on-lagrangian simulation

2013· article· en· W2170437529 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

VenueACM Transactions on Graphics · 2013
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchInstitute for Computing, Information and Cognitive SystemsUniversity of British ColumbiaCanada Research ChairsCanada Foundation for Innovation
KeywordsEulerian pathDiscretizationEuler–Lagrange equationSolverComputer scienceLagrangianApplied mathematicsMathematical optimizationComputer simulationMechanicsMathematicsSimulationMathematical analysisPhysics

Abstract

fetched live from OpenAlex

We describe an Eulerian-on-Lagrangian solid simulator that reduces or eliminates many of the problems experienced by fully Eulerian methods but retains their advantages. Our method does not require the construction of an explicit object discretization and the fixed nature of the simulation mesh avoids tangling during large deformations. By introducing Lagrangian modes to the simulation we enable unbounded simulation domains and reduce the time-step restrictions which can plague Eulerian simulations. Our method features a new solver that can resolve contact between multiple objects while simultaneously distributing motion between the Lagrangian and Eulerian modes in a least-squares fashion. Our method successfully bridges the gap between Lagrangian and Eulerian simulation methodologies without having to abandon either one.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.291
Teacher spread0.263 · 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