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Question
How is AI used in a fusion plant?
Kronos designs an AI-native control stack — a real-time digital twin, anomaly detection, and deterministic safety clamps — to hold the plasma within its envelope faster than a human could.
Fusion is a natural fit for advanced control, and Kronos treats the AI-native plant as a first-class part of the design — while being clear it is a design, not a running system. The digital twin is described on the digital twin and the sensing side on plasma diagnostics.
Questions & answers
What role does AI play in fusion?
Plasma changes faster than any human can react, so a modern fusion plant is controlled by an automated stack. Kronos designs an AI-native control architecture: a real-time digital twin that predicts the plasma 50–100 milliseconds ahead, anomaly-detection models watching the sensor streams, and a deterministic rules engine that clamps every action to physical limits before an actuator moves.
Does AI make it less safe or predictable?
No — the design is built so the fast, safety-critical layers are deterministic, not learned. Machine-learning models advise and predict; a hard rules engine (for example, keeping β_N below its not-to-exceed limit) has final say, and operators retain override authority with a full audit trail.
Is this built and running?
It's a concept-stage control architecture, not an operating system — a software design published alongside the physics. It must ultimately be validated on real machine data, first at the G1 testbed.
Honest gapThe AI control stack is a concept-stage software design; its latency budgets must be demonstrated on production hardware, and its models validated on real machine data, alongside the G1 program.