Omar SobhandClaude Fable 5
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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
35 lines
1.5 KiB
Rust
35 lines
1.5 KiB
Rust
//! Model-order reduction: POD-Galerkin projection with ECSW hyper-reduction.
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//!
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//! The pipeline, all offline steps verified by their own invariants:
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//!
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//! 1. Collect full-order solution snapshots (the caller's job — typically a
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//! parameter or load sweep of [`crate::analysis::NonlinearStaticAnalysis`]).
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//! 2. [`pod::pod_basis`] — orthonormal basis by SVD, truncated at an energy
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//! criterion.
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//! 3. [`ecsw::train_ecsw`] — nonnegative element weights so that a small
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//! element subset reproduces the reduced internal force over the
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//! training set ([`nnls`] with an early stop; sparsity comes from the
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//! stopping tolerance).
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//! 4. [`reduced::ReducedNonlinearModel`] — Newton in reduced coordinates,
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//! assembling only the sampled elements.
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//!
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//! Scope: homogeneous Dirichlet data; the basis is over the free DOFs
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//! only (lifting for inhomogeneous boundary values is not implemented).
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//! Kinematics are selected per [`reduced::Formulation`]: the original
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//! small-strain scope (geometrically linear, materially nonlinear), or
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//! total-Lagrangian Saint Venant–Kirchhoff matching
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//! `NonlinearDynamicAnalysis::with_total_lagrangian` — required when the
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//! snapshots come from a total-Lagrangian trajectory (the FSI flag).
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pub mod dynamic;
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pub mod ecsw;
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pub mod nnls;
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pub mod pod;
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pub mod reduced;
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pub use dynamic::{ReducedNewmark, ReducedState};
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pub use ecsw::{EcswModel, ecsw_residual, train_ecsw, train_ecsw_formulated};
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pub use nnls::nnls;
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pub use pod::pod_basis;
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pub use reduced::{Formulation, ReducedNonlinearModel};
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