Parameter Optimization

Parameter Optimization

Goal

Provide a workflow to design experiments, rank parameter influence, and select optimization strategies for materials simulation calibration.

Requirements

• Python 3.8+
• No external dependencies (uses Python standard library only)

Inputs to Gather

Before running any scripts, collect from the user:

Decision Guidance

Choosing a DOE Method

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)

Choosing an Optimizer

Is dimension <= 5 AND budget <= 100?
├── YES → Bayesian Optimization
└── NO → Is dimension <= 20?
    ├── YES → CMA-ES
    └── NO → Random Search with screening

Script Outputs (JSON Fields)

Workflow

1. Generate DOE with scripts/doe_generator.py
2. Run simulations at DOE sample points (user's responsibility)
3. Summarize sensitivity with scripts/sensitivity_summary.py
4. Choose optimizer using scripts/optimizer_selector.py
5. (Optional) Fit surrogate with scripts/surrogate_builder.py

CLI Examples

# Generate 20 LHS samples for 3 parameters
python3 scripts/doe_generator.py --params 3 --budget 20 --method lhs --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

Conversational Workflow Example

User: I need to calibrate thermal conductivity and diffusivity for my FEM simulation. I can run about 30 simulations.

Agent workflow:

1. Identify 2 parameters → --params 2
2. Budget is 30 → --budget 30
3. Use LHS for general exploration:

   python3 scripts/doe_generator.py --params 2 --budget 30 --method lhs --json
   

4. After user runs simulations and provides outputs, summarize sensitivity:

   python3 scripts/sensitivity_summary.py --scores 0.7,0.3 --names conductivity,diffusivity --json
   

5. Recommend optimizer:

   python3 scripts/optimizer_selector.py --dim 2 --budget 30 --noise low --json
   

Error Handling

Security

The parameter-optimization scripts enforce the following safeguards:

• Parameter name validation: 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 names.
• Input length limits: Comma-separated value lists are capped (10,000 for scores, 100,000 for surrogate data) to prevent resource exhaustion.
• Finite-value enforcement: All numeric list inputs are validated as finite numbers (NaN/Inf rejected).
• Dimension/budget bounds: doe_generator.py caps dim at 1,000 and budget at 1,000,000; optimizer_selector.py caps dim at 100,000 and budget at 10,000,000.
• Reduced tool surface: The skill's allowed-tools excludes Bash to prevent the agent from executing arbitrary commands when processing user-provided parameter names and constraints.

Limitations

• Not for real-time optimization: Scripts provide recommendations, not live optimization loops
• Surrogate is a placeholder: surrogate_builder.py computes basic metrics; replace with actual model for production
• No automatic simulation execution: User must run simulations externally and provide results

References

• references/doe_methods.md - Detailed DOE method comparison
• references/optimizer_selection.md - Optimizer algorithm details
• references/sensitivity_guidelines.md - Sensitivity analysis interpretation
• references/surrogate_guidelines.md - Surrogate model selection

Version History

• v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, conversational examples
• v1.0.0: Initial release with core scripts