# Эксперимент: Filippov Sliding Layer for Neural State-Space Models (#904)

{
  "worked": true,
  "confidence": 9,
  "verdict": "Built and ran a Coulomb-friction oscillator study with hard routing, smooth tanh blending, and a Filippov sliding layer. The mechanism manifested: the exact convex combination had maximum normal residual 0.0 across 20 force-ratio values, smooth residual scaled linearly with force ratio with observed slope 0.50 versus predicted 0.5, and the sliding residual stayed approximately 0.00468 across epsilon values. In rollouts, the sliding layer reduced band residual from 0.423-0.442 for hard routing to 0.00469-0.00471 while hard chatter ranged from 304 to 41 switches per second as dt increased from 0.002 to 0.016.",
  "metrics": {
    "baseline": "Hard Euler routing: band normal residuals 0.4232, 0.4263, 0.4319, 0.4420 at dt=0.002, 0.004, 0.008, 0.016; chatter rates 303.95, 153.95, 78.85, 41.25 switches/s.",
    "idea": "Filippov Euler routing: band residual maxima 0.004688, 0.004691, 0.004697, 0.004710 at the same step sizes; exact identity residual 0.0 and alpha error 0.0. Smooth residual-versus-force-ratio slope was 0.500 with near-zero intercept."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 filippov_experiment.py",
  "files": [
    "filippov_experiment.py",
    "filippov_results.json",
    "run_output.txt",
    "fix_runtime.py"
  ],
  "limitations": "This is a hand-specified two-dimensional oscillator rather than a learned neural state-space model. Only Euler integration was tested, with one deterministic parameter seed; no training, RK4 comparison, reference-solution long-horizon error, GPU execution, or multidimensional learned hyperplane routing was evaluated."
}