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AI Plasma Control

Layer 7: The Predictive Digital Twin

A forward-running simulation of the plasma that forecasts where the current trajectory leads, so the stack can act before limits are reached.

Predicting, not just reacting

Feedback control reacts to error after it appears. A predictive layer runs a fast model of the plasma ahead of real time and asks: given the current state and the current plan, where will we be in a hundred milliseconds, or a second? If the forecast crosses a limit or approaches a disruption boundary, the stack can change course while there is still margin to do so.

What the twin models

Kronos motion — control stack

Fast, reduced, and honest

A predictive layer must run many times faster than real time to be useful, so it uses reduced-order and surrogate models rather than full first-principles simulation. These models are accurate inside the regimes where they were trained or validated and unreliable outside them. The twin therefore reports confidence alongside forecasts and flags extrapolation rather than hiding it.

Closing the outer loop

The twin does not command actuators directly. It advises the scenario layer, which selects among pre-validated responses, and it feeds the disruption-avoidance logic. Keeping the twin out of the fast path means that if it is slow, wrong, or offline, the machine is still protected by the faster deterministic layers below.

Continuous validation

Every discharge is a test of the twin. Its forecasts are logged against what actually happened, and the errors drive re-tuning. A twin that is not continuously scored against reality drifts into confident wrongness, which is more dangerous than obvious ignorance. At Kronos the twin is a simulation and modeling tool for design study; it makes no claim about built hardware.