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Record W2151009771 · doi:10.1002/vis.286

Animating real‐time explosions

2002· article· en· W2151009771 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

VenueThe Journal of Visualization and Computer Animation · 2002
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsElectronic Arts (Canada)
Fundersnot available
KeywordsComputer scienceVoxelSimulationComputer visionComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract Any sufficiently powerful explosion in air creates an expanding blast wave that propagates outwards from the source. The paper explores the physically based simulation of a blast wave impact on surrounding objects. The emphasis is on simplifying the underlying physical and chemical governing equations in order to achieve a visually believable result that performs in real time. A connected voxel model is used to represent objects, so that realistic solid debris is generated instead of flat polygons. The model permits arbitrary voxel shapes, which allow the creation of more complex objects with a lower number of voxels when compared to models using uniform voxel shapes. This model also overcomes certain limitations of the spring–mass particle model when it comes to representing rigid bodies. The paper also explores auxiliary visual effects caused by the blast wave, such as flame, smoke and dust. Each of these cues increases the visual plausibility of the explosion being simulated without being rigorously physically based or computationally intensive. Copyright © 2002 John Wiley & Sons, Ltd.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.197

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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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