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0b4f306ed1 |
rtx-fea: reduced Newmark (mor::dynamic) + the phase-4a offline replay — the ≥10x gate is REFUTED by measurement at the validated resolution
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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 |
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d9d8801f1a |
rtx-fea: mor gains total-Lagrangian operators and a held-out ECSW residual
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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
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8071d5888d |
rtx-fea: ECSW model-order reduction — POD-Galerkin plus hyper-reduction, verified end to end
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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]>
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