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:

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

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)

Method	Best For	Avoid When
lhs	General exploration, moderate dimensions (3-20)	Need exact grid coverage
sobol	Sensitivity analysis, uniform coverage	Very high dimensions (>20)
factorial	Low dimension (<4), need all corners