431 Campaign Patterns 6894dedf

Campaign Patterns

Common patterns for organizing multi-simulation campaigns.

Pattern 1: Parameter Sweep

Use case: Explore parameter sensitivity systematically

campaign/
├── manifest.json      # Sweep configuration
├── campaign.json      # Job tracking state
├── config_0000.json   # Base + dt=0.001
├── config_0001.json   # Base + dt=0.002
├── config_0002.json   # Base + dt=0.003
├── result_0000.json   # Output from job 0
├── result_0001.json   # Output from job 1
└── ...

Workflow:

1. Generate sweep: sweep_generator.py --params "dt:0.001:0.01:10"
2. Initialize campaign: campaign_manager.py --action init
3. Run simulations externally (parallel or sequential)
4. Track progress: job_tracker.py --update
5. Aggregate: result_aggregator.py --metric objective

Pattern 2: Convergence Study

Use case: Verify mesh/time convergence

convergence_study/
├── mesh_10x10/
├── mesh_20x20/
├── mesh_40x40/
├── mesh_80x80/
└── convergence_analysis.json

Approach:

1. Create separate campaigns for each resolution
2. Or use single campaign with resolution as parameter
3. Post-process to compute convergence rates

Pattern 3: Calibration Campaign

Use case: Fit model to experimental data

calibration/
├── iteration_01/      # Initial DOE
│   ├── manifest.json
│   ├── config_*.json
│   └── result_*.json
├── iteration_02/      # Refined search
│   └── ...
└── best_parameters.json

Workflow:

1. Start with DOE sampling (LHS)
2. Run initial batch
3. Identify promising region
4. Refine with focused sweep
5. Repeat until converged

Pattern 4: Ensemble Runs

Use case: Uncertainty quantification with stochastic inputs

ensemble/
├── sample_0000.json   # Random seed 0
├── sample_0001.json   # Random seed 1
├── ...
└── statistics.json    # Mean, std, confidence intervals

Key considerations:

• Use different random seeds for each run
• Aggregate statistics: mean, variance, percentiles
• Check convergence of ensemble statistics

Job Naming Conventions

State Machine for Jobs

                    ┌─────────┐
                    │ pending │
                    └────┬────┘
                         │ start
                         ▼
                    ┌─────────┐
          timeout   │ running │   error
         ┌─────────►│         │◄─────────┐
         │          └────┬────┘          │
         │               │ finish        │
         ▼               ▼               ▼
    ┌─────────┐     ┌─────────┐     ┌─────────┐
    │ timeout │     │completed│     │ failed  │
    └─────────┘     └─────────┘     └─────────┘

File Locking for Parallel Execution

When running jobs in parallel, use lock files to prevent conflicts:

# Job script wrapper
flock -n job_${JOB_ID}.lock python sim.py --config ${CONFIG} || exit 1

Campaign Metadata Best Practices

Always record in manifest.json:

• Creation timestamp
• User/machine identifier
• Git commit hash of simulation code
• Base configuration checksum
• Parameter ranges and method

This enables reproducibility and debugging.