Phase 2 · fleet extensions

KFUSE

BUILT
MULTIFID

multi-fidelity surrogate fusing reduced-twin + mu=400 CGYRO + (pending) real-mass gold, with fidelity-aware uncertainty.

Headline benchmark — honest, computed
multi-fidelity: RMSE 1.481→1.05 (R²=0.787); 3rd fidelity = real-mass gold (deferred)

What it accelerates

KODEX KFUSE is a fast, calibrated surrogate for CGYRO real-mass converged (mu=3672) — 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.

Provenance
CGYRO-urep
Rollout
Phase 2 · Built

Use it

One line, one contract — a prediction, its uncertainty, and whether the input is in-domain.

from kronos_ml import KFUSE

model = KFUSE()                          # loads the trained surrogate
y, sigma, in_domain = model.predict(x)   # y = multifid prediction
if not in_domain:                       # out of its trusted region
    fall_back_to_full_physics()          # KFUSE abstains, never extrapolates

How to trust it

Every 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.

In the fleet

Get it · cite it

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: