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Record W2915022135 · doi:10.5555/3320516.3320687

Building partial differential equations models using cell-devs

2018· article· en· W2915022135 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.

Bibliographic record

VenueWinter Simulation Conference · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsDEVSPartial differential equationPetri netOrdinary differential equationComputer scienceCellular automatonModelicaFormalism (music)Applied mathematicsHybrid systemMathematical optimizationModeling and simulationTheoretical computer scienceDifferential equationComputational scienceAlgorithmMathematicsSimulationMathematical analysis

Abstract

fetched live from OpenAlex

The study of complex systems usually requires hybrid simulations because they have components that are continuous in nature and other that are discrete. It has been proved that DEVS is s a common denominator to combine different Modeling and Simulation methodologies (such as Petri Nets, Cellular Automata, Modelica etc.). We present a cellular model solution to solve PDEs as an extension of classical numerical methods combined with the Cell-DEVS formalism. We explain how to use Cell-DEVS to solve PDEs, focusing on two examples: the PDEs of a Heat Diffusion Process solved with the Method of Lines, and the Shallow Water Equations solved using the Lax-Wendroff method. We will discuss the advantages of solving PDEs with Cell-DEVS, in particular its integration with other hybrid models.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.333
GPT teacher head0.464
Teacher spread0.131 · 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