Transfer Learning Across Devices
Models trained on existing fusion devices give Kronos machines a head start, provided the transfer respects where the physics differs.
Learning before you operate
Kronos machines are pre-construction, so they produce no operating data yet. But decades of data exist from other fusion devices. Transfer learning uses models trained on that data as a starting point, adapting them to the Kronos design rather than learning from nothing once the machines run.
What transfers and what does not
- General plasma behavior and instability physics often transfer well.
- Device-specific geometry and scaling may not.
- Diagnostic signatures depend on the specific instrumentation.
- The tandem-mirror burner shares less with tokamak data than the Hyperion breeder does.
Adapting responsibly
A model transferred without adaptation can be confidently wrong on a new device. Transfer learning fine-tunes the model on whatever target-relevant data exists, including simulation from the Kronos design, and characterizes where the transfer is trustworthy. The differences are respected, not averaged over.
Simulation as a bridge
Where real target data is absent, the digital twin provides synthetic data specific to the Kronos design, bridging the gap between other devices and the machine that does not exist yet. This is how control and analysis tools mature before first plasma.
The payoff
When the Hyperion breeder reaches first tritium around 2030, its models are already informed rather than blank, shortening the time to reliable operation and feeding the intelligence flywheel.
Honesty
A transferred model's validity is tested against target data as it becomes available, and its predictions carry uncertainty that reflects the transfer, never presented as if trained on the target directly.