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Record W4413010775 · doi:10.1109/tvcg.2025.3596334

Thunderstruck: Visually Simulating Electrical Storms

2025· article· en· W4413010775 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

VenueIEEE Transactions on Visualization and Computer Graphics · 2025
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsComputer scienceStormVisualizationComputer graphics (images)Data visualizationArtificial intelligenceGeology

Abstract

fetched live from OpenAlex

Thunderstorms are complex multiphysics phenomena driven by charge transfer processes arising from interactions between ice and water particles in the atmosphere. We present a physically grounded model for simulating cloud electrification and lightning discharge, capable of generating diverse lightning types as emergent responses to evolving atmospheric conditions. Our approach requires only a minimal set of atmospheric parameters and no user-defined triggers. Charge separation is modeled at the microphysical level using a statistical mechanics framework, while discharges are captured through a novel gauge-invariant dielectric breakdown model that accounts for bipolar channels, dynamic electric fields, and air resistance. We validate our method through comparisons with observational data and prior models, demonstrating its ability to simulate distinct discharge types and the full life cycle of thunderstorms. Beyond scientific accuracy, our framework supports real-time nowcasting, civil engineering assessments, virtual environment generation, and the simulation of complex dielectric breakdown in varied contexts.

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: none
Teacher disagreement score0.978
Threshold uncertainty score0.777

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 communication0.0000.000
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 head0.009
GPT teacher head0.251
Teacher spread0.242 · 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