Phase 3 · roadmap
impurity-seeding radiation evaluator — maps a seeded impurity + radiated-power fraction to core Zeff penalty and P_rad from the H9 seeding scan (N / Ne / Ar; Ar gives the least core Zeff dilution at f_rad 0.58). Small scan; full impurity-transport (SOLPS + impurity) is the fidelity upgrade.
KODEX KRAD is a fast, calibrated surrogate for impurity transport (SOLPS + impurity) + radiation control — 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 KRAD
model = KRAD() # loads the trained surrogate
y, sigma, in_domain = model.predict(x) # y = radiation-control prediction
if not in_domain: # out of its trusted region
fall_back_to_full_physics() # KRAD 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: