The Twin Refinement Loop
How discrepancies between the KRONOS-CTRL twin and reality are captured, diagnosed offline, and folded back into better models.
Closing the loop between fast and slow
The twin refinement loop is the feedback path that keeps the real-time twin honest. When the KRONOS-CTRL twin's 50 to 100 ms prediction diverges from what actually happens, that residual is a signal. L0 collects these residuals, diagnoses their cause, and refines the models so the next version predicts better. It is how the twin learns from its own mistakes.
Residual as data
Every predicted-versus-actual mismatch, in the Power, Neutronics, Thermomechanics, or MHD module, is logged with full context: the inputs, the prediction, the outcome, and the diagnostic state. Aggregated over many pulses, these residuals reveal where a surrogate is systematically wrong, which is far more useful than any single miss.
- Log predicted-versus-actual residuals per twin module
- Attribute error to model, data, or genuine novelty
- Enrich the training set with the informative cases
- Refine, revalidate, and promote through retraining
Diagnosis before retraining
Not every residual means the model is wrong. It might reflect a bad sensor, a data-fabric issue, or genuinely new machine behavior. The loop diagnoses the cause first, because retraining on mislabeled or corrupted residuals degrades the model. Only cleanly attributed discrepancies are folded into the next retraining cycle.
The loop is deliberately offline. Refinement requires re-solving physics, curating data, and revalidating, none of which can happen inside a real-time control budget. The twin runs fast and fixed during operation; it improves only between deployments, through this slow, careful L0 process.
For both machines, the loop is what turns operating experience into better prediction. Until FOAK around 2030 it runs against simulated pulses and study campaigns, exercising the refinement machinery so that when real breeder and burner pulses arrive, the loop is already trusted and ready to learn from them.