Phase 2 · fleet extensions
tensor-network / low-rank (MPS-style SVD) ROM of the real CGYRO A1e flux database. **Honest measured result:** the flux is only *modestly* compressible — effective rank ~2.93 of 4; bond-dim-2 keeps ~91% of the variance but ~30% rel-L2, and the turbulent↔quiet transition is a sharp high-rank feature. A genuine ROM characterization that *corrects* the naive "flux is trivially low-rank" expectation. Classical SVD/MPS — **no quantum-advantage claim**.
KODEX KTENSOR is a fast, calibrated surrogate for exact many-body / quantum simulation — 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 KTENSOR
model = KTENSOR() # loads the trained surrogate
y, sigma, in_domain = model.predict(x) # y = tn prediction
if not in_domain: # out of its trusted region
fall_back_to_full_physics() # KTENSOR 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: