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 › L3 · Twin Modeling & AI
L3 · Twin Modeling & AI

Anomaly-Detection Ensembles

No single detector is reliable enough for safety-relevant precursors, so Kronos combines reconstruction, density, and forecasting detectors into a calibrated ensemble.

THE STACK · click to jumpL7Ecosystem & StrategyL6Experience & VisualizationL5Applications & CopilotsL4OrchestrationL3Twin Modeling & AIL2Data FabricL1Control PlaneL0Foundation▲tlmctl▼L3 · TWIN MODELING & AIThe KRONOS-CTRL digital twin and its predictive shadow.1KRONOS-CTRL Twinlive plant state2GNNscoupled subsystems3PINNsphysics-constrained4Anomaly Ensemblesdrift & fault detection5MPCreceding-horizon control6Predictive Shadowruns seconds aheadMACHINE TIEState estimate descends to L1 control; alerts rise to L4 / L5.KRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORTWIN MODELING & AISHEET 05REV. 2026-08L3 · AI-NATIVE STACK
L3 · Twin Modeling & AI — its place in the stack (left, click any layer) and its internal components (right). Telemetry rises; control descends.

Why an ensemble

Sub-threshold precursors are rare, varied, and partly novel, exactly the regime where any single anomaly model has blind spots. Kronos runs an ensemble of complementary detectors so that a precursor invisible to one is likely visible to another, and combines their scores with a calibrated aggregator.

Combining scores

Raw detector scores are on different scales, so each is calibrated to a probability, then combined. Kronos weights detectors by their validated reliability on the relevant failure class and requires agreement for high-confidence alarms, which suppresses the single-detector false positives that would erode operator trust.

python
# calibrated ensemble score
p_k   = calibrate_k(score_k(x))        # per-detector -> probability
w_k   = validated_reliability[k]        # from V&V on that failure class
p_ens = sum_k w_k * p_k / sum_k w_k
alarm = (p_ens > tau) and (n_agreeing >= m)   # require agreement

The ensemble does not only watch magnets. It watches disruption precursors for the breeder (locked-mode phase patterns on the Mirnov graph), plug-density excursions for the burner, divertor thermal anomalies, and fuel-cycle isotope-balance drift. Each failure class has its own tuned detector mix, but all report into the same calibrated framework so operators see one coherent risk picture.

Relation to the twin

The forecast-residual detector is the tightest coupling to KRONOS-CTRL: it compares the live signals against the twin's 50-100 ms shadow prediction, so an anomaly is precisely a place where reality diverges from validated physics. That is often the earliest and most physically meaningful precursor of all.

Content reviewed August 2026 · design-and-simulation stage