Эксперимент: Exact Event-Chained Neural ODE (#585)

{ "worked": true, "confidence": 8, "verdict": "Built a two-phase exact-chained neural trajectory surrogate for a switched scalar ODE, with analytic and autograd checks. Chained interface defects were exactly zero, the phase residual was numerically zero, and the soft-interface defect matched |q2-q1|/(1+2λ) to numerical precision across tested jumps and penalty weights. The chained model improved post-event RMSE for jumps 1, 3, and 6, but was worse with no event, so the result is promising for abrupt events rather than a universal win.", "metrics": { "baseline": "Single MLP post-event RMSE: jump 0=0.00168, 1=0.00834, 3=0.03159, 6=11.4420", "idea": "Exact chained MLP post-event RMSE: jump 0=0.00689, 1=0.00587, 3=0.02874, 6=11.0804; interface defect=0 for all tested jumps; soft-defect scaling relative error <=1.5e-14" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json", "run_output.txt" ], "limitations": "Only a scalar switched linear ODE with supervised trajectory fitting was tested. The full swing-equation pendulum, noisy observations, multidimensional states, residual-only PINN training, gradient variance, FLOPs, and a learned soft-chaining neural baseline were not tested." }