# Эксперимент: Rankine–Hugoniot Front Tokens (#1136)

{
  "worked": true,
  "confidence": 7,
  "verdict": "Built a self-contained Burgers front-token MVP with jump detection, local Rankine–Hugoniot speed evaluation, analytical front propagation, piecewise rendering, and a Godunov finite-volume baseline. The RH identity was reproduced to machine precision, and analytical tokens had essentially zero front and field error on the clean shock while the baseline errors decreased with resolution. This is a clear toy signal, but it is not evidence yet for learned residuals, attention, source terms, front interactions, or a fair equal-compute neural comparison.",
  "metrics": {
    "baseline": "Mean over N={64,128,256}: front-position L1=0.00867 and terminal field L1=0.00557; per-resolution front errors=[0.01468,0.00760,0.00373].",
    "idea": "Mean over N={64,128,256}: front-position L1=5.22e-16 and terminal field L1=0.0; RH identity maximum error=0.0; one-step position errors were 0.0 for dt={0.002,0.004,0.008}."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 front_tokens_experiment.py",
  "files": [
    "front_tokens_experiment.py",
    "results.json"
  ],
  "limitations": "The test uses a single clean scalar Burgers shock with exact constant traces, so the token branch is effectively oracle-like and does not train a neural residual or attention module. It does not test source terms, rarefactions, multiple interacting fronts, noisy detection, autoregressive learned rollouts, equal-parameter/FLOP neural baselines, or the learned-versus-analytic position ablation."
}