ClarusC64/infrastructure-temporal-5node-pressure-buf-lag-cpl-grid-stress-blackout-v0.1

What this repo does

This dataset tests whether a model can detect a power grid stress cascade forming over time and predict whether blackout lock-in occurs by the final step.

Core quad

pressure buffer lag coupling

Prediction target

labelcascadestate

Row structure

One row represents a short time window (t0–t3) of grid stress conditions including demand pressure, reserve buffer margin, response lag, and interconnect coupling tightness. The label marks whether cascade lock-in (blackout formation) is reached by t3.

Files

data/train.csv data/tester.csv scorer.py

Evaluation

Run predictions on tester.csv Score with scorer.py

License

MIT

Structural Note

This dataset identifies a measurable coupling pattern associated with systemic instability. The sample demonstrates the geometry. Production-scale data determines operational exposure.

What Production Deployment Enables

• 50K–1M row datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support

Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration

Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.

For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.