Simulation Validator

Goal

Provide a three-stage validation protocol: pre-flight checks, runtime monitoring, and post-flight validation for materials simulations.

Requirements

• Python 3.10+
• No external dependencies (uses Python standard library only)
• Works on Linux, macOS, and Windows

Inputs to Gather

Before running validation scripts, collect from the user:

Decision Guidance

When to Run Each Stage

Is simulation about to start?
├── YES → Run Stage 1: preflight_checker.py
│         └── BLOCK status? → Fix issues, do NOT run simulation
│         └── WARN status? → Review warnings, document if accepted
│         └── PASS status? → Proceed to run simulation
│
Is simulation running?
├── YES → Run Stage 2: runtime_monitor.py (periodically)
│         └── Alerts? → Consider stopping, check parameters
│
Has simulation finished?
├── YES → Run Stage 3: result_validator.py
│         └── Failed checks? → Do NOT use results
│                            → Run failure_diagnoser.py
│         └── All passed? → Results are valid

Choosing Validation Thresholds

Script Outputs (JSON Fields)

Three-Stage Validation Protocol

Stage 1: Pre-flight (Before Simulation)

1. Run scripts/preflight_checker.py --config simulation.json
2. BLOCK status: Stop immediately, fix all blocker issues
3. WARN status: Review warnings, document accepted risks
4. PASS status: Proceed to simulation

Note: preflight_checker.py validates required keys, numeric ranges,
output-directory access, and disk space. It does not evaluate numerical
stability (CFL / diffusion-Fourier). For explicit stability gating use
skills/core-numerical/numerical-stability/scripts/cfl_checker.py.

python3 scripts/preflight_checker.py \
    --config simulation.json \
    --required dt,dx,kappa \
    --ranges "dt:1e-6:1e-2,dx:1e-4:1e-1" \
    --min-free-gb 1.0 \
    --json

Stage 2: Runtime (During Simulation)

1. Run scripts/runtime_monitor.py --log simulation.log periodically
2. Configure alert thresholds based on problem type
3. Stop simulation if critical alerts appear

python3 scripts/runtime_monitor.py \
    --log simulation.log \
    --residual-growth 10.0 \
    --dt-drop 100.0 \
    --json

Stage 3: Post-flight (After Simulation)

1. Run scripts/result_validator.py --metrics results.json
2. All checks PASS: Results are valid for analysis
3. Any check FAIL: Do NOT use results, diagnose failure

python3 scripts/result_validator.py \
    --metrics results.json \
    --bound-min 0.0 \
    --bound-max 1.0 \
    --mass-tol 1e-3 \
    --json

For variational / gradient-flow models (Allen-Cahn, Cahn-Hilliard), add --variational to enforce a strict monotone non-increasing energy check.

Failure Diagnosis

When validation fails:

python3 scripts/failure_diagnoser.py --log simulation.log --json

Conversational Workflow Example

User: My phase field simulation crashed after 1000 steps. Can you help me figure out why?

Agent workflow:

1. First, check the log for obvious errors:

``bash python3 scripts/failure_diagnoser.py --log simulation.log --json ``

1. If diagnosis suggests numerical blow-up, check runtime stats:

``bash python3 scripts/runtime_monitor.py --log simulation.log --json ``

1. Recommend fixes based on findings:

- If residual grew rapidly → reduce time step - If dt collapsed → check stability conditions - If NaN detected → check initial conditions

Error Handling

Interpretation Guidance

Status Meanings

Confidence Score Interpretation

A requested bound (--bound-min/--bound-max) with no matching fieldmin/fieldmax in the metrics is reported as a failed boundsunverifiable check, never a vacuous pass. For variational/gradient-flow runs, pass --variational (or set "energyvariational": true in the metrics) to enforce a strict monotone non-increasing energy check (energymonotone); otherwise a weaker energynet_decrease check is used, which does not detect mid-run spikes.

Common Failure Patterns

Verification checklist

Do not trust a validation verdict until each applicable item below is satisfied with the concrete artifact named. Record these in your summary to the user.

• Ran resultvalidator.py --json and confirmed results.status is PASS (not INSUFFICIENTDATA) AND results.confidencescore == 1.0; a null score or INSUFFICIENTDATA means no check ran — treat as unverified, not as a pass.
• Listed results.checks and confirmed every requested check actually appears (e.g. massconserved, boundssatisfied, nonan, and energymonotone/energynetdecrease); confirmed results.failedchecks is empty and contains no boundsunverifiable entry (which means a requested bound had no fieldmin/fieldmax to compare against).
• For variational/gradient-flow models (Allen-Cahn, Cahn-Hilliard), passed --variational (or set "energyvariational": true) so energymonotone is enforced; recorded that the weaker energynetdecrease was NOT relied on, since it cannot detect mid-run energy spikes.
• Recorded the mass drift tolerance used (--mass-tol, default 1e-3) and confirmed it matches the Conservative/Standard/Relaxed column appropriate to the run; did not silently accept the default for a tight-conservation problem.
• Ran runtimemonitor.py --json and recorded residualstats (min/max/last) and dt_stats; confirmed there are no alerts for NaN/Inf/overflow, residual growth above --residual-growth, or dt collapse below --dt-drop.
• Confirmed numerical stability was gated separately via core-numerical/numerical-stability/scripts/cflchecker.py (CFL/Fourier limit) — preflightchecker.py does NOT evaluate CFL/Fourier and a PASS preflight says nothing about temporal/spatial stability.
• On any FAIL or alert, ran failurediagnoser.py --json and recorded the probablecauses/recommended_fixes, rather than reusing the results.

Common pitfalls & rationalizations

Security

Input Validation

• Config file paths are validated for existence before parsing; non-existent paths produce clear errors (exit code 2)
• --required parameter names are validated against a safe-character allowlist (^[A-Za-z0-9_.-]+$); names with shell metacharacters are rejected
• --ranges entries are parsed as name:min:max with finite numeric bounds enforced and max > min required
• --min-free-gb is validated as a finite positive number (negatives, zero, nan, inf rejected)
• --residual-growth and --dt-drop thresholds are validated as finite positive numbers
• --bound-min and --bound-max are validated as finite numbers (nan/inf rejected), and --bound-max > --bound-min is enforced; --mass-tol is validated as a finite positive number
• Invalid input exits with code 2 and an explanatory message

File Access

• preflight_checker.py reads a single user-specified config file (JSON/YAML) and checks disk space on the volume hosting the resolved output directory
• runtime_monitor.py reads a single log file specified by --log; log files are size-limited (500 MB max) and rejected before parsing if larger
• result_validator.py reads a single metrics file (JSON) specified by --metrics
• failure_diagnoser.py reads a single log file specified by --log; log files are size-limited (500 MB max) before parsing
• No scripts write to the filesystem; all output goes to stdout

Tool Restrictions

• Read: Used to inspect script source, references, config files, and simulation logs
• Bash: Used to execute the four Python validation scripts (preflightchecker.py, runtimemonitor.py, resultvalidator.py, failurediagnoser.py) with explicit argument lists
• Write: Used to save validation reports; writes are scoped to the user's working directory
• Grep/Glob: Used to locate log files, config files, and search references

Safety Measures

• No eval(), exec(), or dynamic code generation
• All subprocess calls use explicit argument lists (no shell=True)
• failurediagnoser.py uses hardcoded, pre-compiled diagnostic regex patterns; runtimemonitor.py accepts optional --residual-pattern / --dt-pattern overrides that are compiled with re.compile (no eval) and applied only to the user's own log
• Diagnostic strings emitted in output are drawn from the skill's fixed cause/fix table, not interpolated from raw log content

Limitations

• Not a real-time monitor: Scripts analyze logs after-the-fact
• Regex-based: Log parsing depends on pattern matching; may miss unusual formats
• No automatic fixes: Scripts diagnose but don't modify simulations

References

• references/validation_protocol.md - Detailed checklist and criteria
• references/log_patterns.md - Common failure signatures and regex patterns

Version History

• v1.2.2 (2026-06-24): Added a Verification checklist (evidence-based, tied to the four scripts' JSON outputs) and a Common pitfalls & rationalizations table to harden agent interpretation of validation verdicts.
• v1.2.0 (2026-06-23): Corrected diagnostic regexes (no false convergence/blow-up on healthy logs), direction-aware dt-collapse detection, NaN/Inf scan in runtime monitor, strict variational energy check, non-vacuous bounds/confidence, config-relative output-dir + correct-volume disk check, and implemented the documented input-validation/file-size safeguards
• v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, Windows compatibility
• v1.0.0: Initial release with 4 validation scripts