Phase 1 · data-backed core

KYRO

BUILT
TRANSPORT

CGYRO turbulence-transport surrogate (ion heat flux Q_i vs a/L_T x shear); representative fidelity mu=400, real-mass gold pending.

Headline benchmark — honest, computed
complete 16/16 map (12 turbulent / 4 quiet (16/16 complete)); turbulent/quiet 16/16 correct, R²=0.858 / cov90=0.875; 0.329 ms vs 2.89 GPU-h/pt (μ=400)

What it accelerates

KODEX KYRO is a fast, calibrated surrogate for CGYRO (nonlinear gyrokinetic) — 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 1 · Built

Use it

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

from kronos_ml import KYRO

model = KYRO()                          # loads the trained surrogate
y, sigma, in_domain = model.predict(x)   # y = turbulent heat flux Q
if not in_domain:                       # out of its trusted region
    fall_back_to_full_physics()          # KYRO 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: