Cross-Machine and Cross-Device Transfer Learning
Because the GNN is permutation-equivariant and topology-driven, embeddings pretrained on legacy device data transfer to both Kronos machines.
Why transfer is possible
A GNN keyed on physical relationships, not on fixed channel indices, learns operators (how a flux-surface link propagates information, how mechanically coupled strain gauges co-move) that are device-agnostic. That means an embedding pretrained on public tokamak magnetic and Thomson data captures general MHD structure that is reusable, and only the Kronos-specific graph and heads need fine-tuning.
The transfer path
Kronos pretrains message-passing weights on legacy spherical-tokamak and mirror-experiment archives at L0 (offline HPC, batch retraining), then fine-tunes on high-fidelity simulation of the actual breeder and burner geometries. Because the machines are still design/simulation studies pre-FOAK, simulation is the dominant fine-tuning source today; once FOAK first tritium arrives ~2030, real telemetry progressively replaces simulated fine-tuning data.
- Pretrain: legacy device magnetics/Thomson, general MHD operators
- Adapt: swap the graph (breeder toroidal vs burner mirror geometry)
- Fine-tune: Kronos multi-physics simulation, then FOAK telemetry
- Freeze: shared message-passing core; retrain only task heads
The breeder-to-burner transfer is the more interesting case. The two machines have different confinement physics, but share diagnostic modalities (Thomson, interferometry, magnetics, REBCO strain, neutron flux) and the same L2 feature conventions. The imputation and denoising heads transfer almost directly; the physics-interpretation heads (mode identification vs plug-density inference) are retrained per machine.
Transfer is validated, not assumed: every transferred model passes the same validation-and-verification gate as a from-scratch model before it is allowed to inform the twin, and confidence scoring is recalibrated on target-machine data so an over-confident transferred prediction cannot slip through.