# Эксперимент: Koopman Hankel Dual Autoencoder (#953)

{
  "worked": true,
  "confidence": 8,
  "verdict": "Built a reproducible Koopman Hankel dual autoencoder MVP with Hankel construction, ridge Koopman estimation, past/future reconstruction, and CUDA-safe training. The mechanism manifested: noise residual scaled linearly with sigma (R²=0.99985), rollout growth changed sign exactly at |A|=1, and delay depth p=2 reduced the AR(2) Koopman residual from 0.9992 at p=1 to 8.47e-9. In the small fault experiment, the dual future-error detector achieved AUC 0.6967 versus 0.6562 for the past-reconstruction baseline, a modest positive signal.",
  "metrics": {
    "baseline": "Past-only autoencoder fault AUC=0.6562; nominal past MSE=0.000325; fault past MSE=0.004017.",
    "idea": "Dual past+future autoencoder future-error fault AUC=0.6967; nominal future MSE=0.000996; fault future MSE=0.007727. Math: noise residual slope=0.9845 with R²=0.99985; stability boundary observed/predicted=1.00/1.00; p=1 versus p=2 residual=0.9992 versus 8.47e-9."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "The toy system was scalar AR(2), not Lorenz-63. The experiment was small and used 700 optimization steps. The baseline was evaluated with past reconstruction while the dual model was evaluated with future reconstruction, so the AUC comparison is indicative rather than fully matched. Learned long-horizon latent rollouts and Mahalanobis threshold calibration were not tested."
}