Эксперимент: Invariant-domain learned reconstruction (#468)

{ "worked": true, "confidence": 9, "verdict": "Built a deterministic invariant-domain limiter for 2-D ideal-gas conserved states using an explicit density bound and pressure bisection. The mechanism manifested: density-limit error was 0, pressure-root error was at most 2.22e-16, and Lambda*theta remained constant with tail ratio 1.0 across a 32x raw-strength sweep. In the secondary random test, raw invalid states occurred 56.0% of the time while limited states had 0% invalid states; this toy test does not establish an Euler accuracy win.", "metrics": { "baseline": "First-order cell-average reconstruction: 56.0% raw invalid rate in generated raw outputs; L2 distance to raw output 3.3401; cell reconstruction error 0 by definition.", "idea": "Invariant limiter: 0.0% invalid states, 56.0% activation rate, L2 distance to raw output 1.0646, L2 distance to cell average 2.2755; density-bound max error 0, pressure-root max error 2.22e-16, inverse-scaling tail ratio 1.0." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "invariant_reconstruction.py", "run_experiment.py", "results.json" ], "limitations": "No neural network training, finite-volume Euler time integration, shock-location or L1 accuracy study, GPU benchmark, unstructured mesh, or comparison against a true minmod/Venkatakrishnan reconstruction was performed. The baseline comparison is only a small conserved-state reconstruction sanity test." }