# Эксперимент: Dissipative Neural State-Space Identification (#1213)

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built a compact Duffing neural state-space identification MVP with a nonnegative learned storage function, squared positive-part dissipativity penalty, 100-step rollout evaluation, and a direct telescoping sanity check. The math check passed numerically: maximum telescoping error was 7.1e-15 and the constructed certified bound had slack of about -20. In the fixed-seed mini-experiment, the dissipative model had worse MSE than the unconstrained baseline (0.1619 vs 0.0341 at 100 steps; 0.001406 vs 0.001326 at 10 steps), while 59.8% of residuals remained positive and mean residual was 0.01445, so the promised stability/accuracy effect was not observed.",
  "metrics": {
    "baseline": "Duffing test MSE: 0.001326 at 10 steps, 0.034096 averaged over 100 steps; cumulative squared error 3.4096",
    "idea": "Duffing test MSE: 0.001406 at 10 steps, 0.161861 averaged over 100 steps; cumulative squared error 16.1861; positive residual fraction 0.598; mean residual 0.01445"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "Only a small damped Duffing toy system and one fixed random seed were tested; Lorenz-63, noisy-data sweeps, fixed/free initialization gaps, FLOPs, plots, hyperparameter searches, and longer training were not evaluated. The baseline residual fields are intentionally NaN because no storage certificate is defined for the baseline."
}