Numerical Integration

SkillDev tools

Select and configure time integration methods for ODE and PDE simulations — choose among explicit Runge-Kutta, BDF, Rosenbrock, and Adams families, set relative and absolute error tolerances, implement adaptive step-size control with P/PI step-size controllers, plan IMEX operator splitting for mixed stiff and non-stiff terms, and estimate splitting errors. Use when picking an integrator for a new simulation, diagnosing step rejections or tolerance failures, setting up operator splitting for phase-field or reaction-diffusion problems, or deciding between explicit and implicit time marching, even if the user only says "my solver keeps rejecting steps" or "which ODE method should I use."

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Numerical Integration skill

What this skill tells your AI

The instructions your AI receives, as published by heshamfs/materials-simulation-skills in skills/core-numerical/numerical-integration/SKILL.md and read by ahel’s review.

Goal

Provide a reliable workflow to select integrators, set tolerances, and manage adaptive time stepping for time-dependent simulations.

Requirements

  • Python 3.10+
  • NumPy (for some scripts)
  • No heavy dependencies for core functionality

Inputs to Gather

InputDescriptionExample
Problem typeODE/PDE, stiff/non-stiffstiff PDE
Jacobian availableCan compute ∂f/∂u?yes
Target accuracyDesired error level1e-6
ConstraintsMemory, implicit allowed?implicit OK
Time scaleCharacteristic time1e-3 s

Decision Guidance

Choosing an Integrator

Is the problem stiff?
├── YES → Is Jacobian available?
│   ├── YES → Use Rosenbrock or BDF
│   └── NO → Use BDF with numerical Jacobian
└── NO → Is high accuracy needed?
    ├── YES → Use RK45 or DOP853
    └── NO → Use RK4 or Adams-Bashforth

Stiff vs Non-Stiff Detection

SymptomLikely StiffAction
dt shrinks to tiny valuesYesSwitch to implicit
Eigenvalues span many decadesYesUse BDF/Radau
Smooth solution, reasonable dtNoStay explicit

Script Outputs (JSON Fields)

ScriptKey Outputs
scripts/error_norm.pyerror_norm, scale_min, scale_max
scripts/adaptive_step_controller.pyaccept, dt_next, factor
scripts/integrator_selector.pyrecommended, alternatives, notes
scripts/imex_split_planner.pyimplicit_terms, explicit_terms, splitting_strategy
scripts/splitting_error_estimator.pyerror_estimate, substeps

Workflow

  1. Classify stiffness - Check eigenvalue spread or use stiffness_detector
  2. Choose tolerances - See references/tolerance_guidelines.md
  3. Select integrator - Run scripts/integrator_selector.py
  4. Compute error norms - Use scripts/error_norm.py for step acceptance
  5. Adapt step size - Use scripts/adaptive_step_controller.py
  6. Plan IMEX/splitting - If mixed stiff/nonstiff, use scripts/imex_split_planner.py
  7. Validate convergence - Repeat with tighter tolerances

Conversational Workflow Example

User: I'm solving the Allen-Cahn equation with a stiff double-well potential. What integrator should I use?

Agent workflow:

  1. Check integrator options:
    python3 scripts/integrator_selector.py --stiff --jacobian-available --accuracy high --json
    
  2. Plan the IMEX splitting (diffusion implicit, reaction explicit):
    python3 scripts/imex_split_planner.py --stiff-terms diffusion --nonstiff-terms reaction --coupling weak --json
    
  3. Recommend: Use IMEX-BDF2 with diffusion term implicit, double-well reaction explicit.

Pre-Integration Checklist

  • Identify stiffness and dominant time scales
  • Set rtol/atol consistent with physics and units
  • Confirm integrator compatibility with stiffness
  • Use error norm to accept/reject steps
  • Verify convergence with tighter tolerance run

CLI Examples

# Select integrator for stiff problem with Jacobian
python3 scripts/integrator_selector.py --stiff --jacobian-available --accuracy high --json

# Compute scaled error norm
python3 scripts/error_norm.py --error 0.01,0.02 --solution 1.0,2.0 --rtol 1e-3 --atol 1e-6 --json

# Adaptive step control with PI controller
python3 scripts/adaptive_step_controller.py --dt 1e-2 --error-norm 0.8 --order 4 --controller pi --json

# Plan IMEX splitting
python3 scripts/imex_split_planner.py --stiff-terms diffusion,elastic --nonstiff-terms reaction --coupling strong --json

# Estimate splitting error
python3 scripts/splitting_error_estimator.py --dt 1e-4 --scheme strang --commutator-norm 50 --target-error 1e-6 --json

Error Handling

ErrorCauseResolution
rtol must be a non-negative finite number / atol must be a non-negative finite numberInvalid tolerancesUse non-negative finite values
error_norm must be finite and non-negativeNegative or non-finite error normCheck error computation
scale must be positive; with min_scale=0 ensure atol>0 or rtol*|y|>0All scale entries collapsed to 0 (e.g. rtol=0, atol=0, default min_scale=0)Set atol>0, rtol>0, or --min-scale > 0
argument --controller: invalid choice: ... (choose from p, pi)Invalid controller typeUse p or pi
Provide at least one stiff or non-stiff termEmpty term listSpecify stiff or nonstiff terms

Interpretation Guidance

Error Norm Values

Error NormMeaningAction
< 1.0Step acceptableAccept, maybe increase dt
≈ 1.0At tolerance boundaryAccept with current dt
> 1.0Step rejectedReject, reduce dt

Controller Selection

ControllerCLI valuePropertiesBest For
P (elementary / integral)p (default)Simple, some overshootNon-stiff, moderate accuracy
PI (proportional-integral)piSmooth, robust (requires --prev-error)General use

PID control is described in references/error_control.md for reference only; the adaptive_step_controller.py CLI implements p and pi controllers.

IMEX Strategy

CouplingStrategy
WeakSimple operator splitting
ModerateStrang splitting
StrongFully coupled IMEX-RK

Verification checklist

  • Recorded the scaled error_norm (and scale_min/scale_max) from scripts/error_norm.py and confirmed scale_min > 0, so no component reduced to atol-only scaling by accident.
  • Confirmed each accepted step has error_norm <= accept_threshold (default 1.0) per scripts/adaptive_step_controller.py; logged any accept: false steps and the resulting dt_next/factor rather than forcing the step through.
  • For the PI controller, supplied --prev-error and verified controller_used reported pi (not the silent p fallback that occurs when --prev-error is omitted).
  • Ran scripts/integrator_selector.py with the actual --stiff/--jacobian-available/--accuracy flags matching the problem and recorded recommended plus the notes (e.g. the "expect smaller dt" warning for stiff-without-implicit).
  • For operator splitting, recorded error_estimate, substeps, and dt_effective from scripts/splitting_error_estimator.py and confirmed error_estimate <= target-error after substepping, using the correct --scheme order (lie=1, strang=2).
  • Ran the convergence validation (Workflow step 7): repeated with tighter rtol/atol and confirmed the solution change is below the target accuracy, rather than trusting a single tolerance run.

Common pitfalls & rationalizations

Tempting shortcutWhy it's wrong / what to do
"I picked an implicit/BDF method, so any dt is fine."Unconditional stability is not accuracy; large dt still inflates temporal error. Check error_norm against the accept threshold and run the tighter-tolerance convergence check (Workflow step 7).
"error_norm came back < 1, so the step and the whole run are correct."The norm only certifies the local step under the chosen rtol/atol. A loose tolerance passes every step while the global solution is wrong — tighten tolerances and confirm convergence before trusting results.
"I'll use the PI controller for smoother stepping" but omit --prev-error.Without --prev-error the script silently falls back to the P controller (controller_used: p). You get no PI benefit. Pass the previous accepted error_norm and verify controller_used: pi.
"Strang vs Lie splitting won't matter much here."Splitting error scales as commutator_norm * dt^(order+1) with order 1 (lie) vs 2 (strang). For a nonzero commutator the schemes differ by a full power of dt — run splitting_error_estimator.py with the actual --commutator-norm and --target-error instead of guessing.
"The selector recommended IMEX/RK-Chebyshev, so I'll just run explicitly without a Jacobian."That branch fires only for stiff problems without implicit solves and the script warns "expect smaller dt." Read the notes: provide a Jacobian/Jv product and use BDF/Radau if implicit solves are feasible.
"It ran to the final time without crashing, so the integration is valid."Completion is not correctness. Verify step acceptance (error_norm <= threshold), splitting error within target-error, and convergence under tighter tolerances; for conservative problems pass --conservative to imex_split_planner.py and check conserved quantities.

Security

Input Validation

  • All numeric inputs (dt, rtol, atol, error_norm, stiffness_ratio, commutator_norm, etc.) are validated as finite numbers at the function boundary
  • imex_split_planner.py validates term names against [a-zA-Z_][a-zA-Z0-9_ -]* with length and count limits, preventing injection payloads in user-supplied term lists
  • Comma-separated value lists are capped at 100,000 entries to prevent resource exhaustion
  • Numeric bounds enforced: dimension capped at 10 billion, order at 20, stiffness_ratio at 1e30
  • --controller is validated against a fixed allowlist (p, pi)
  • --scheme is validated against known splitting schemes (lie, strang)

File Access

  • Scripts read no external files; all inputs are provided via CLI arguments
  • Scripts write only to stdout (JSON output); no files are created unless the agent explicitly uses the Write tool

Tool Restrictions

  • Read: Used to inspect script source, references, and user configuration files
  • Write: Used to save integrator recommendations or splitting plans; writes are scoped to the user's working directory
  • Grep/Glob: Used to locate relevant files and search references
  • The skill's allowed-tools excludes Bash to prevent the agent from executing arbitrary commands when processing user-provided inputs

Safety Measures

  • No eval(), exec(), or dynamic code generation
  • All subprocess calls use explicit argument lists (no shell=True)
  • Reduced tool surface (no Bash) limits the agent to read/write operations only
  • Term names are sanitized before use, preventing shell metacharacter injection

Limitations

  • No automatic stiffness detection: Use stiffness_detector from numerical-stability
  • Splitting assumes separability: Terms must be cleanly separable
  • Jacobian requirement: Some methods need analytical or numerical Jacobian

References

  • references/method_catalog.md - Integrator options and properties
  • references/tolerance_guidelines.md - Choosing rtol/atol
  • references/error_control.md - Error norm and adaptation formulas
  • references/imex_guidelines.md - Stiff/non-stiff splitting
  • references/splitting_catalog.md - Operator splitting patterns
  • references/multiphase_field_patterns.md - Phase-field specific splits

Version History

  • v1.2.2 (2026-06-24): Added Verification checklist and Common pitfalls & rationalizations sections grounding step acceptance, PI controller usage, integrator selection, and splitting-error checks in the scripts' actual outputs
  • v1.2.0 (2026-06-23): Fixed PI controller coefficient/sign to standard form, fixed error_norm scale-collapse crash, corrected error-message and controller documentation to match the scripts
  • v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, examples
  • v1.0.0: Initial release with 5 integration scripts

Signals

GitHub stars
66
Forks
3
Last commit
Jun 2026
Advanced
Catalog kind
skill
Gateway key
numerical-integration
Source
github.com/heshamfs/materials-simulation-skills