Closed-loop inverse-design validation. Given a target deformation field, solve the inverse problem on a trained surrogate (Ridge / linear), then run an Abaqus FEA verification and compare surrogate-predicted vs. true displacement field. Reports MSE / MAE / max-abs-error / NRMSE side-by-side, plus saturated-channel count, so you can quantify the surrogate-FEA gap. Use when the user wants to evaluate "is my surrogate good enough for inverse design?", "how big is the surrogate-FEA gap on this target?", "did the optimizer find a real solution or just a surrogate hallucination?"
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Closed-loop inverse-design validation. Given a target deformation field, solve the inverse problem on a trained surrogate (Ridge / linear), then run an Abaqus FEA verification and compare surrogate-predicted vs. true displacement field. Reports MSE / MAE / max-abs-error / NRMSE side-by-side, plus saturated-channel count, so you can quantify the surrogate-FEA gap. Use when the user wants to evaluate "is my surrogate good enough for inverse design?", "how big is the surrogate-FEA gap on this target?", "did the optimizer find a real solution or just a surrogate hallucination?"
Closes the verification loop on a surrogate-driven inverse design. Surrogates are fast but optimistic — they extrapolate, hallucinate, and reward saturated solutions that the real FEA cannot reproduce. This skill forces a side-by-side comparison between what the surrogate thinks it found and what Abaqus actually delivers, on the same target shape.
When to Use This Skill
Activate when the user wants to:
Sanity-check a surrogate that was just trained ("is the model trustworthy?")
Quantify the surrogate-FEA gap on a fixed set of validation targets before publishing claims
Compare optimizer choices (PGD vs. L-BFGS-B vs. multi-start) under equal-FEA-budget conditions
Generate a reproducible benchmark table for a paper / report
Debug why an optimizer's surrogate solution looks great but FEA verification fails
Do NOT use this skill for:
Forward-only FEA runs without a surrogate (use abaqus-lhs-batch-dataset)
Surrogate training (this skill assumes X_amplitude.csv + Y_grid_uz.csv already exist; pair with the dataset / grid skills upstream)
Real-time hardware-in-the-loop (FEA validation is too slow; use a different loop)
The Loop
target shape (N x N csv)
│
│ load + bilinear resample to learning grid
│ scale to reachable peak amplitude
▼
y_target (flattened, N²)
│
│ standardize with (y_mean, y_std) from training data
▼
y_target_std
│
│ solve argmin_z ||z @ W - y_target_std||² + λ ||z||²
│ subject to z_lo ≤ z ≤ z_hi (standardized box constraint)
│ solver ∈ { PGD, L-BFGS-B, multistart L-BFGS-B, Nelder-Mead }
▼
z_sol (standardized solution)
│
│ unstandardize: x_sol = z_sol * x_std + x_mean
│ hard-clip to [bounds_min, bounds_max]
▼
x_sol (the design vector to physically realize)
│
├──── surrogate forward predict ────► y_pred
│ │
│ │ vs. target
│ ▼
│ surrogate_metrics
│ (MSE, MAE, max_abs, NRMSE)
│
├──── write ForceAmplitude.dat
│ copy_template_inputs(template_dir, case_dir)
│ subprocess: abaqus cae noGUI="<solver_script>"
│ extract_grid(node_displacement.csv, N, N)
│ │
│ │ vs. target
│ ▼
│ true_metrics
│ (MSE, MAE, max_abs, NRMSE)
▼
summary.csv: surrogate_metrics + true_metrics + saturated_channels + return_code
The key signal is the gap between surrogate_metrics and true_metrics. A small gap means the surrogate is faithful; a large gap means it's overfitting or extrapolating into unphysical regions.
Required Inputs
The user must provide:
Aggregated training data (typically from the abaqus-odb-to-grid-csv skill upstream):
Target shape file(s) — one or more N×N CSV / TXT matrices of the desired deformation field. Common formats:
Plain matrix CSV (no header, N rows × N columns)
Headered CSV with uz_0000..uz_(N²-1) columns (single row)
template_dir/ + solver_script.py — same as the abaqus-lhs-batch-dataset skill. Required for the FEA verification step.
Design bounds[bounds_min, bounds_max] (e.g. [-0.5, +0.5]) — must match the bounds the training data was sampled from. Mismatched bounds will produce saturated solutions that the FEA cannot realize.
Target peak amplitude — most published targets are normalized. Scale them to a peak the surrogate's training range can actually produce (e.g. target_peak = 2.5 mm if training data uz spans ±3 mm).
The 4 Inverse Solvers
For a linear Ridge surrogate y_std = z @ W, the inverse problem is convex quadratic. Pick the solver based on your needs:
Solver
When to use
Iters / cost
Notes
PGD
Fastest, deterministic, no scipy needed
1200 fixed steps
Good baseline; sensitive to lr. Default for stdlib-only environments.
L-BFGS-B
Best convergence per iteration; needs scipy
~50-200 iters
Initialize from closed-form solution; converges in O(D) on linear surrogates. Recommended default.
multistart L-BFGS-B
Avoids saddle / boundary local minima
n_starts × ~100 iters
Use when D is large (>50) or bounds are tight (saturated_channels > D/4).
Nelder-Mead
Derivative-free fallback; debug only
5000 fevals
Slowest, no gradient; only useful when you suspect bugs in the gradient path.
For nonlinear surrogates (MLP), only L-BFGS-B and Nelder-Mead are practical (the closed-form initialization step doesn't apply).
The same (x_mean, x_std, y_mean, y_std) used during training must be used at validation. Saving them to a .npz next to the trained surrogate avoids skew.
2. Hard-clip after unstandardize
z_sol lives in standardized space and respects (z_lo, z_hi). After converting back to x_sol, always re-clip to [bounds_min, bounds_max] because numerical drift can produce values like 0.5000001 that would crash the FEA's amplitude validation.
3. saturated_channels is the early-warning metric
Count entries within tol=1e-6 of the bounds. If > D/4 channels are saturated, the surrogate is asking the optimizer to extrapolate beyond the training distribution. The FEA will likely diverge or produce nonsense. Lower target_peak and re-run; don't trust either set of metrics in this regime.
4. NRMSE normalization choice
norm_mse = mse / max(|y_target|)² (the target_max_abs mode) makes errors directly comparable across targets of different magnitudes. Always specify the normalization in any reported number. Other valid choices: target_range = max(y_target) - min(y_target).
5. The 4 modes of failure
Mode
Symptom
Diagnosis
Surrogate hallucination
small surrogate_mse, large true_mse
Saturated channels, training data too narrow, or nonlinearity not captured
FEA divergence
rc != 0, true metrics = NaN
Amplitudes too aggressive — reduce target_peak or tighten bounds
Both fail
both metrics large
Target shape itself unreachable in the design space; check whether the basis can express it at all
Both succeed but disagree
small surrogate_mse, small true_mse, but predicted-uz heatmap differs from FEA-uz heatmap
Mode-mixing — the L-BFGS-B found a local optimum the surrogate likes but the FEA reaches differently. Try multi-start.
6. FEA cost dominates total runtime
Surrogate inverse solve takes ~milliseconds. Each FEA verification takes 2-5 minutes. Cache the surrogate fit (write Ridge weights to model_ridge.npz once) and reuse across targets. Do not re-fit on every target.
7. Reproducibility
Set numpy.random.seed(42) for any solver with stochastic initialization (multi-start). Record the seed in summary.csv. Without this, the multi-start results are not reproducible across runs.
Reference Implementation
A complete, dependency-light Python implementation is in references/surrogate_validation.py (~400 lines). It supports all 4 solvers, is parameterized via argparse, and produces the side-by-side summary CSV.
references/inverse_solvers.py — the 4 inverse-solver implementations (PGD pure stdlib + numpy; L-BFGS-B / multi-start / Nelder-Mead via scipy).
Output Schema
work_root/
├ surrogate_inverse_summary.csv # one row per target, all metrics side-by-side
├ target_dome/
│ ├ target_scaled_NxN.csv # the rescaled target the optimizer aimed at
│ ├ inverse_solution.csv # x_sol + scale_factor + saturated_channels
│ ├ predicted_surrogate_NxN.csv # what the surrogate said x_sol would produce
│ ├ predicted_true_NxN.csv # what Abaqus actually produced (final frame)
│ ├ ForceAmplitude.dat # the per-case design vector for FEA
│ ├ Membrane2D1.odb # FEA result
│ ├ node_displacement.csv # raw Abaqus output
│ ├ summary.csv # all metrics for this target
│ └ run_*.log
├ target_saddle/
└ ...
The 3 NxN CSVs (target_scaled, predicted_surrogate, predicted_true) are designed for direct heatmap plotting via matplotlib.imshow. Their per-cell errors are the most diagnostic visualization for "is the surrogate trustworthy" questions.
Quick Sanity Checks
After a validation run completes:
Saturation rate: average saturated_channels / D across targets — if > 30%, your bounds or target_peak are wrong, redo with tighter peak before trusting any metric
Gap statistics: mean(true_norm_mse) / mean(surrogate_norm_mse) — if > 3.0, the surrogate is over-confident; consider an MLP or richer feature basis
FEA success rate: sum(return_code == 0) / N_targets — should be > 90%; if lower, diagnose run_stderr.log of failures (typically convergence / mesh distortion)
Spot-check: pick one target with the largest gap, plot the 3 heatmaps side-by-side. The error structure (smooth offset / oscillation / boundary artifact) tells you whether to add training data, regularize more, or change the surrogate class.
Pairs Well With
abaqus-lhs-batch-dataset (upstream): produces the sample_*/ directories
abaqus-odb-to-grid-csv (upstream): produces the X_amplitude.csv + Y_grid_uz.csv this skill consumes
abaqus-job / abaqus-odb (peer skills from JaimeCernuda/abaqus-scripting): for hand-debugging individual failed validation cases
