2 Commits

Include renames

Author	SHA1	Message	Date
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