Phase 2 · fleet extensions
heating & current-drive actuator-response surrogate — RandomForest over a 3888-point current-drive design scan; predicts driven current I_cd from RF/NBI drive parameters + plasma state (**R²=0.949**). Feeds KAIROS heating control. Reduced CD model — RF/NBI ray-tracing is the fidelity upgrade.
KODEX KHEAT is a fast, calibrated surrogate for full RF/NBI ray-tracing (GENRAY/TORAY/NUBEAM) — reproducing its result at inference speed, so it runs inside a real-time control loop or a design search where the full computation is far too slow to call.
One line, one contract — a prediction, its uncertainty, and whether the input is in-domain.
from kronos_ml import KHEAT
model = KHEAT() # loads the trained surrogate
y, sigma, in_domain = model.predict(x) # y = heating&cd prediction
if not in_domain: # out of its trusted region
fall_back_to_full_physics() # KHEAT abstains, never extrapolatesEvery KODEX code wraps the shared spine — KHALO for calibrated uncertainty and KGATE for the out-of-domain gate — so it reports how confident it is and abstains rather than extrapolate. Benchmarks are computed on held-out data with a fixed seed; pre-registered misses are kept, not hidden.
Part of the open kronos-ml package (Apache-2.0). Open-access deposits with citable DOIs are listed below.
Open-access deposits, each with a citable DOI: