Skip to content
Technology How it works Breeder — Hyperion Burner — Aegis Burner — MetroVolt AI-Native Architecture Magnets Fuel cycle Safety Roadmap
Solutions AI & Data Centers Defense & Government Grid & Baseload Neutron Detection Quantum
Learn Technical Library
Proof Publications Whitepapers Technical Library Open Science & Reproducibility The Honest Gates
Company About / Mission Leadership Environment Health & Safety Investors Careers Press Contact
3D Model
AI Architecture › L2 · Data Fabric
L2 · Data Fabric

Quench & Disruption Precursor Features

Sub-threshold signatures engineered from magnetics, strain, and profile telemetry give the anomaly ensembles a head start on quench and disruption.

THE STACK · click to jumpL7Ecosystem & StrategyL6Experience & VisualizationL5Applications & CopilotsL4OrchestrationL3Twin Modeling & AIL2Data FabricL1Control PlaneL0Foundation▲tlmctl▼L2 · DATA FABRICTelemetry, validation, and the machine's memory.160+ Port Telemetrysensor bus2Signal Validationrange & sanity3Feature Engineeringderived signals4Time-Series Archivefull history5Feature Storetraining-ready6Vector DBembeddings for RAGMACHINE TIEIngests from diagnostics; serves the twin (L3) and copilots (L5).KRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORDATA FABRICSHEET 04REV. 2026-08L2 · AI-NATIVE STACK
L2 · Data Fabric — its place in the stack (left, click any layer) and its internal components (right). Telemetry rises; control descends.

Seeing trouble early

A quench in a REBCO magnet or a disruption in the breeder plasma is preceded by faint, sub-threshold structure in the telemetry: a growing Mirnov mode, a locking magnetic island, a strain delta drifting, a pressure profile peaking toward a stability limit. The fabric engineers these into explicit precursor features so the L3 anomaly ensembles operate on physics-shaped inputs rather than raw traces.

Breeder precursors

Burner precursors

Why engineer, not just learn

The anomaly ensembles could in principle learn from raw data, but physics-grounded precursor features make them faster, more sample-efficient, and interpretable. When an ensemble fires, an operator can see which precursor drove it. The features also give the twin's predictive shadow (running 50-100 ms ahead) something concrete to forecast.

These are the features that make the sub-10 microsecond control boundary meaningful: the fabric surfaces the precursor early enough that L1 actuation, or the autonomous failsafe, can respond. Everything here is validated in simulation for machines whose FOAK operation is expected near 2030.

Content reviewed August 2026 · design-and-simulation stage