Phase 3 · roadmap
divertor / edge heat-flux → target-thermal surrogate — maps divertor heat flux to target surface temperature + material limits (W / CuCrZr) from the H9 exhaust scan. q→T_surf fit **R²=1.0** (77 pts); CuCrZr material limit **q~13.5 MW/m²**. Reduced 0-D thermal model — SOLPS-ITER/EIRENE edge campaign is the fidelity upgrade.
KODEX KEDGE is a fast, calibrated surrogate for SOLPS-ITER / EIRENE edge campaign — 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 KEDGE
model = KEDGE() # loads the trained surrogate
y, sigma, in_domain = model.predict(x) # y = edge-transport prediction
if not in_domain: # out of its trusted region
fall_back_to_full_physics() # KEDGE 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: