Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection. Use for calibration, uncertainty studies, parameter sweeps, LHS sampling, Sobol analysis, surrogate modeling, or Bayesian optimization setup.
Provide a workflow to design experiments, rank parameter influence, and select optimization strategies for materials simulation calibration.
Before running any scripts, collect from the user:
| Input | Description | Example |
|-------|-------------|---------|
| Parameter bounds | Min/max for each parameter with units | kappa: [0.1, 10.0] W/mK |
| Evaluation budget | Max number of simulations allowed | 50 runs |
| Noise level | Stochasticity of simulation outputs | low, medium, high |
| Constraints | Feasibility rules or forbidden regions | kappa + mobility < 5 |
Is dimension <= 3 AND full coverage needed?
├── YES → Use factorial
└── NO → Is sensitivity analysis the goal?
├── YES → Use quasi-random (preferred; "sobol" is accepted but deprecated)
└── NO → Use lhs (Latin Hypercube)
| Method | Best For | Avoid When |
|--------|----------|------------|
| lhs | General exploration, moderate dimensions (3-20) | Need exact grid coverage |
| quasi-random | Sensitivity analysis, uniform coverage (preferred) | Very high dimensions (>20) |
| sobol | Deprecated alias of quasi-random (emits a warning) | New code (use quasi-random) |
| factorial | Low dimension (<4), need all corners | High dimension (exponential growth) |
Factorial sizing: the factorial grid is
levelsevenly spaced values per parameter, producing exactlylevels ** paramssamples. Set the resolution explicitly with--levels(e.g.--params 2 --levels 4-> 16 samples). If you use--budgetinstead, the script back-computeslevels = round(budget ** (1/params))and warns whenever the realized sample count differs from the requested budget (e.g.--budget 20 --params 2realizes 16 samples). For an exact design, pass a perfect power (--budget 16) or, preferably,--levels.
Is dimension <= 10 AND budget <= 100?
├── YES → Bayesian Optimization
└── NO → Is dimension <= 20?
├── YES → CMA-ES
└── NO → Random Search with screening
| Noise Level | Recommendation | |-------------|----------------| | Low | Gradient-based if derivatives available, else Bayesian Optimization | | Medium | Bayesian Optimization with noise model | | High | Evolutionary algorithms or robust Bayesian Optimization |
| Script | Output Fields |
|--------|---------------|
| scripts/doe_generator.py | samples, method, coverage (count, dimension; plus levels and a top-level requested_budget/note for factorial) |
| scripts/optimizer_selector.py | recommended, expected_evals, notes |
| scripts/sensitivity_summary.py | ranking, notes |
| scripts/surrogate_builder.py | model_type, metrics (mse, cv_error, output_variance), notes |
scripts/doe_generator.pyscripts/sensitivity_summary.pyscripts/optimizer_selector.pyscripts/surrogate_builder.py# Generate 20 LHS samples for 3 parameters
python3 scripts/doe_generator.py --params 3 --budget 20 --method lhs --json
# Full factorial with 4 levels per parameter (2 params -> 16 samples)
python3 scripts/doe_generator.py --params 2 --levels 4 --method factorial --json
# Rank parameters by sensitivity scores
python3 scripts/sensitivity_summary.py --scores 0.2,0.5,0.3 --names kappa,mobility,W --json
# Get optimizer recommendation for 3D problem with 50 eval budget
python3 scripts/optimizer_selector.py --dim 3 --budget 50 --noise low --json
# Build surrogate model from simulation data
python3 scripts/surrogate_builder.py --x 0,1,2 --y 10,12,15 --model rbf --json
User: I need to calibrate thermal conductivity and diffusivity for my FEM simulation. I can run about 30 simulations.
Agent workflow:
--params 2--budget 30python3 scripts/doe_generator.py --params 2 --budget 30 --method lhs --json
python3 scripts/sensitivity_summary.py --scores 0.7,0.3 --names conductivity,diffusivity --json
python3 scripts/optimizer_selector.py --dim 2 --budget 30 --noise low --json
| Error | Cause | Resolution |
|-------|-------|------------|
| params must be positive | Zero or negative dimension | Ask user for valid parameter count |
| budget must be positive | Zero or negative budget | Ask user for realistic simulation budget |
| argument --method: invalid choice: <value> (choose from lhs, sobol, quasi-random, factorial) | Invalid method (argparse) | Use decision guidance to pick a valid method |
| could not convert string to float: <token> | Non-numeric value in --scores/--x/--y | Reformat as 0.1,0.2,0.3 |
| scores must be a comma-separated list | Empty --scores input | Provide at least one numeric score |
doe_generator.py coverage.count and confirmed it matches the intended design — for factorial, verified count == levels ** params and that no note/requested_budget mismatch warning was emitted (or that the realized count is acceptable).--method matches the Decision Guidance for the actual dimension/goal, and that quasi-random was used instead of the deprecated sobol alias (no DeprecationWarning in output).optimizer_selector.py recommended strategy and expected_evals, and verified expected_evals <= budget so the plan is feasible within the stated evaluation budget.sensitivity_summary.py ranking and checked whether the top sensitivity is < 0.1 (the "All sensitivities are low" note); if so, did not over-interpret the ranking and revisited the output metric.metrics.cv_error (leave-one-out), NOT in-sample mse — especially for rbf, where mse is near zero by construction — and compared cv_error against metrics.output_variance to confirm the surrogate beats the constant-mean baseline.cv_error is a finite number (not NaN), i.e. there were enough samples for leave-one-out (poly: n > degree+1; rbf: n >= 3).| Tempting shortcut | Why it's wrong / what to do |
|-------------------|------------------------------|
| "RBF surrogate mse is ~0, so the model is excellent." | RBF is an exact interpolant — in-sample mse is near zero by construction and says nothing about generalization. Judge fit with metrics.cv_error and compare it to output_variance. |
| "I asked for --budget 20 factorial, so I got 20 samples." | Factorial honors levels ** params, not the budget; --budget 20 --params 2 realizes 16 samples and emits a note/warning. Use --levels for an exact, intended design. |
| "sobol gives me a true Sobol low-discrepancy sequence." | sobol is a deprecated alias that emits a DeprecationWarning and uses a simplified golden-ratio additive recurrence, not a true Sobol sequence. Use quasi-random; for production Sobol use scipy.stats.qmc. |
| "The optimizer recommendation is just advice — budget doesn't matter." | The recommendation is gated on dimension AND budget (BO only for dim<=10 AND budget<=100), and expected_evals is capped at the budget. Record both and confirm the plan fits the real budget. |
| "One sensitivity score is highest, so that parameter dominates." | The script only sorts the scores you pass in; it computes no sensitivity itself. If the top score is < 0.1 it flags that all sensitivities are low — get the scores from a real screening/Sobol analysis before trusting the ranking. |
| "It printed JSON without erroring, so the result is valid." | Exit success only means inputs parsed. Verify the design size, expected_evals <= budget, a finite cv_error, and that the surrogate beats output_variance before trusting any output. |
sensitivity_summary.py validates --names against [a-zA-Z_][a-zA-Z0-9_ .-]* with a 200-char limit, preventing shell metacharacter injection via crafted parameter namesNaN/Inf rejected)doe_generator.py caps dimension at 1,000 and budget at 1,000,000; optimizer_selector.py caps dimension at 100,000 and budget at 10,000,000--method is validated against a fixed allowlist (lhs, quasi-random/sobol, factorial); sobol is an accepted but deprecated alias of quasi-random--noise is validated against a fixed allowlist (low, medium, high)--model (surrogate type) is validated against a fixed allowlist (rbf, poly)--levels (factorial grid resolution) is validated as an integer in [2, 1000]allowed-tools excludes Bash to prevent the agent from executing arbitrary commands when processing user-provided parameter names and constraintseval(), exec(), or dynamic code generationshell=True)surrogate_builder.py fits a real 1-D least-squares polynomial (poly) or Gaussian RBF interpolant (rbf) using only the standard library and reports honest residual mse, leave-one-out cv_error, and the data output_variance; for production use scipy/scikit-learn/GPyTorch. For rbf, in-sample mse is near zero by construction (exact interpolation) — judge fit quality with cv_errorreferences/doe_methods.md - Detailed DOE method comparisonreferences/optimizer_selection.md - Optimizer algorithm detailsreferences/sensitivity_guidelines.md - Sensitivity analysis interpretationreferences/surrogate_guidelines.md - Surrogate model selectionpoly least-squares, rbf interpolation) with honest mse/cv_error/output_variance; explicit factorial --levels with budget-mismatch warnings; BO dimension cutoff harmonized to dim<=10; corrected Security/Error-Handling/output-field docs to match script behaviornpx skills add HeshamFS/parameter-optimization下载完整 Skill 目录，包含 SKILL.md 及所有相关文件
Edit PDFs with natural-language instructions using the nano-pdf CLI.
Control Sonos speakers (discover/status/play/volume/group).
Terminal Spotify playback/search via spogo (preferred) or spotify_player.
Capture frames or clips from RTSP/ONVIF cameras.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Monitor blogs and RSS/Atom feeds for updates using the blogwatcher CLI.
Category:tools