Model & Simulation Validation Checklist

Model & Simulation Validation Checklist

Purpose: Use this checklist to make sure computational models are trustworthy, their limitations are explicit, and validation evidence is recorded. Adapt items to your domain, scale, and regulatory needs. Collect evidence (test outputs, plots, version hashes) as you go.

1. Code verification (is the implementation correct?)

• Unit tests: cover numerical kernels, boundary conditions, I/O, and key utilities. Record test coverage and a passing baseline.
• Regression tests: add tests for previously fixed bugs to avoid reintroduction.
• Reproducible runs: fix random seeds, record timestamps, and capture computational environment (OS, language/runtime version, library versions, compiler flags).
• Automated CI: ensure model tests run in a continuous integration environment or repeatable container image.

2. Validation dataset selection and independence

• Define validation objectives and acceptance criteria before seeing validation data (example: target RMSE < X, coverage of 90% within predicted intervals).
• Use independent validation data that were not used for calibration or model selection; prefer true out-of-sample or held-back experiments.
• Document provenance of datasets: source, preprocessing steps, missing-data treatment, and any transformations.
• When only limited data are available, use cross-validation strategies but note limitations vs independent experiments.

3. Sensitivity analysis

• Identify key input parameters and model assumptions expected to influence outputs.
• Use global sensitivity methods (e.g., Sobol, Morris) where feasible, or one-at-a-time as an initial screen.
• Report sensitivity ranks and ranges; flag parameters with high influence and poor identifiability.
• If outputs are highly sensitive to poorly known inputs, record that as a limitation and prioritize data collection or robust design.

4. Calibration protocol and reporting

• Describe the calibration dataset, objective function, optimizer settings, parameter bounds, and stopping criteria.
• Report calibration results with uncertainty (e.g., parameter posteriors, bootstrap intervals, or covariance estimates).
• Avoid overfitting: compare calibration vs validation performance and prefer simpler models when performance is comparable.
• Archive calibration runs (inputs, parameter sets, diagnostics) and link them to specific code commits.

5. Uncertainty quantification (UQ)

• Distinguish aleatory (stochastic) vs epistemic (knowledge) uncertainty and state which you quantify.
• Use appropriate UQ methods: Monte Carlo, bootstrap, Bayesian posterior sampling, interval analysis, or ensembles as appropriate.
• Report predictive intervals, coverage probabilities, and sensitivity of uncertainty to modeling choices.
• Where possible, propagate input and parameter uncertainty through to decision-relevant outputs.

6. Benchmarking and performance metrics

• Benchmark against empirical data and credible baselines (simpler models or historical performance).
• Choose clear metrics aligned with objectives (e.g., RMSE, MAE, bias, calibration error, AUC, F1, decision-cost metrics).
• Report both point estimates and distributional performance (e.g., boxplots across scenarios or bootstraps).
• Document failure modes: where and why the model fails, and expected conditions outside the validated range.

7. Documentation, provenance & reproducibility

• Record model purpose, version, authors, data sources, assumptions, and known limitations in a single 'validation report' or README.
• Use version control for code and data where feasible; tag releases associated with validation artifacts.
• Capture an execution manifest: exact command, container image or environment spec, seed values, and randomization details.
• Provide runnable examples or notebooks that reproduce key validation figures and metrics.

8. Peer review, traceability & sign-off

• Have an independent reviewer (internal or external) inspect validation choices, datasets, and results.
• Maintain an issues log for outstanding validation concerns and mitigation plans.
• Define acceptance and escalation rules (who signs off, what evidence satisfies acceptance, and when the model must be retired).

9. Ongoing monitoring and lifecycle

• Define post-deployment monitoring metrics and thresholds to detect model drift or degraded predictive performance.
• Plan periodic revalidation after major data, process, or environment changes.
• Track model lineage: what changed, why, and how validation evidence was updated for each change.

How to use this checklist

Walk through items during model development milestones: pre-implementation, pre-release, and post-deployment reviews. Collect artifacts (test logs, figures, validation datasets, signed reports) and store them with the model version. For teams: assign owners for verification, validation data, UQ, and documentation steps.

Examples of acceptance cues (adapt to context)

• Holdout dataset: predictive RMSE within pre-specified bounds and predictive intervals with >90% empirical coverage for critical outputs.
• Calibration vs validation: no large degradation (e.g., validation error < 1.2 × calibration error) unless justified.
• Sensitivity: key decision outputs stable across plausible parameter ranges or uncertainty explicitly captured and acceptable to stakeholders.

Notes: Tailor numeric thresholds to your domain and risk tolerance. This checklist is a practical starting point — stronger regulatory or safety domains will require formal protocols and documentation.