4 Commits

Include renames

Author	SHA1	Message	Date
Omar SobhandClaude Fable 5	0b4f306ed1	rtx-fea: reduced Newmark (mor::dynamic) + the phase-4a offline replay — the ≥10x gate is REFUTED by measurement at the validated resolution ... The dynamic layer over ReducedNonlinearModel: reduced consistent mass V'MV (full element sum, never ECSW-sampled — ECSW weights are trained on internal-force virtual work and would conserve the wrong inertia), reduced_force_and_jacobian exposed (solve() refactored onto it), and ReducedNewmark mirroring NonlinearDynamicStepper::newmark_newton in reduced coordinates (same predictor, residual, tangent shape; no rescue ladder by design — a reduced Newton death is a finding). TDD (tests/reduced_newmark.rs): identity-basis march reproduces the full stepper to 2.4e-14 over 15 steps (both Newton loops tightened to 1e-10 so only solver rounding separates them); rigid-translation reduced mass = rho*A to 1e-9; a 6-mode POD basis tracks its training trajectory at 4.2e-4 rms against a 1.0e-4 projection floor. Phase 4a (fsi3_ecsw_offline.rs, fsi3_reduced_newmark_replay, env-gated): reduced Newmark replay of the harvested FSI3 trajectory at record cadence (dt_rec = 5x march dt), driven by the recorded end-of-step loads. Measured, m=12/20: - COST (dt-independent, the verdict): 3,068/3,580 us/step at 4.6/5.0 Newton iters — 2.0-2.3x the banded full-order structural step (7,200 us/pass, bandedlu_fsi3_ny62_t85). The >=10x gate needs <=720 us/step; one reduced eval alone costs ~640 us because phase 2 refuted hyperreduction (every eval loops all 70 elements). The gate arithmetic is closed: reduced Newton needs >=2 evals, capping the ROM at ~5x. THE CAMPAIGN GATE (pinned cycle bands at >=10x structural speedup) CANNOT BE MET at the validated resolution. - TRACKING at record cadence diverges in the release transient (dies t=4.35-4.45) — and the RTX_REPLAY_IDENTITY control dies EARLIER (t=4.13) in the exact subspace: the death is the 5x-coarse integration + aliased loads, NOT the reduction. The record-cadence replay cannot judge subspace dynamics; the projection floor (1.1e-3 at m=12) remains the honest subspace statement. Campaign verdict to be recorded in omni-cortex in the pre-registered words. Co-Authored-By: Claude Fable 5 <[email protected]> Claude-Session: https://claude.ai/code/session_01X2GmJXeQ2njUecEKiJZ1G2	2026-08-30 07:07:49 -05:00
Omar SobhandClaude Fable 5	50e382c046	rtx-fsi: ECSW phase-2 offline study — POD subspace confirmed, hyperreduction refuted at this resolution ... fsi3_ecsw_offline.rs (env-gated on RTX_ECSW_SNAP; committed default is a no-op) measures the three pre-registered quantities on the harvested run-7 flag trajectory, with a numbering self-check (clamped-DOF displacement exactly 0.0 across all 3,294 snapshots). Measured: (1) the flapping manifold compresses 560 free DOFs to 12-20 POD modes at ~0.5% held-out rms projection error — the subspace exists. (2) ECSW has NOTHING TO EXPLOIT on the 70-element flag: the NNLS residual sits at single-element scale until nearly every element joins (31 el -> 10%, 51 -> 10%, 67 -> 2.5%, 70 -> 1e-15; held-out == training everywhere; cycle-only manifold identical) — each macroscopic Quad8 carries non-redundant virtual work, so no sub-percent sample smaller than the mesh exists. (3) full reduced assembly 548 us/eval; ECSW at best 1.4x at a useless 10% residual. ReducedNonlinearModel gains assemble_reduced_force (the measured quantity). The campaign's >=10x structural gate cannot come from hyperreduction at the validated resolution — recorded in the campaign doc with the re-scope options. Co-Authored-By: Claude Fable 5 <[email protected]> Claude-Session: https://claude.ai/code/session_01X2GmJXeQ2njUecEKiJZ1G2	2026-08-29 15:06:33 -05:00
Omar SobhandClaude Fable 5	d9d8801f1a	rtx-fea: mor gains total-Lagrangian operators and a held-out ECSW residual ... The mor scope was small-strain only ("geometrically linear") — a basis trained on total-Lagrangian trajectories (the FSI flag marches with_total_lagrangian) sampled through small-strain operators would conserve the virtual work of the wrong force. ElementOperator now carries a Formulation (SmallStrain | TotalLagrangian), the TL branch mirroring NonlinearDynamicAnalysis exactly (SVK from Lame parameters, total_lagrangian::internal_force_and_tangent); train_ecsw / ReducedNonlinearModel::new keep their behavior and delegate, with _formulated variants added. ecsw_residual evaluates a trained model's ||Cw - b||/||b|| on arbitrary snapshots — the held-out generalization measurement; on the training set it reproduces training_residual to 1e-12 (pinned). Verified sharply: identity-basis reduced TL solve vs a tight-tolerance full TL solve agrees to 4.4e-15 (machine precision) while small-strain operators land 1.1e-2 away at the same load — the switch is exercised and exact. (At the default 1e-6 convergence criteria the reference itself stops 1.7e-4 short; measured and recorded in the test comment.) Co-Authored-By: Claude Fable 5 <[email protected]> Claude-Session: https://claude.ai/code/session_01X2GmJXeQ2njUecEKiJZ1G2	2026-08-29 15:01:09 -05:00
Omar SobhandClaude Fable 5	8071d5888d	rtx-fea: ECSW model-order reduction — POD-Galerkin plus hyper-reduction, verified end to end ... The third Farhat gap. New rtx_fea::mor module: - pod::pod_basis — orthonormal SVD basis with an energy-criterion truncation. Verified: rank-2 data yields exactly 2 orthonormal modes that reconstruct every snapshot to machine precision; a loose tolerance truncates a dominant-mode-plus-noise set to one mode. - nnls — Lawson-Hanson non-negative least squares with the early stop that makes ECSW work: iteration ends at the requested residual, and the active-set structure caps the support at one column per outer iteration, so sparsity falls out of the stopping tolerance. Verified against KKT conditions, exact positive solutions, negative-clipping, and a sparsity-vs-tolerance case. Its thresholds are RELATIVE to the problem's own scales — the first version used absolute cutoffs (1e-14) that silently ended the iteration on ECSW's small-magnitude training systems at 1.2e-3 instead of the requested 1e-4. - ecsw::train_ecsw — element weights such that a small subset reproduces the reduced internal force (the virtual work against the basis) over the training snapshots. w = 1 solves the system exactly by construction, so it is always consistent; nonnegativity is what keeps a sampled element from producing energy. - reduced::ReducedNonlinearModel — Newton in POD coordinates, assembling either every element (POD-Galerkin) or the ECSW sample, on the same per-element force/tangent machinery the nonlinear analysis uses. End-to-end verification (tests/ecsw_mor.rs): a clamped nonlinear block, snapshots from a 4-point load sweep, evaluated at an UNSEEN load factor: POD modes: 2 ECSW sample: 5 of 24 elements training residual 2.2e-7 (requested 1e-4) error vs full solve: POD-Galerkin 3.09e-7, ECSW 3.08e-7 hyper-reduction cost (ECSW vs full ROM): 1.6e-8 And the assertion with the most teeth: the same 5 elements with their weights forced to 1 read a relative error of 1.22 — a completely wrong field — so the accuracy is carried by the WEIGHTS, not by the subset happening to be representative. Scope, stated plainly: geometrically linear, materially nonlinear, homogeneous Dirichlet only (no lifting); the basis lives on the free DOFs. 559 rtx-fea tests, 0 failing. Co-Authored-By: Claude Fable 5 <[email protected]>	2026-08-20 00:43:32 -07:00