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numerical-stability

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Numerical Stability

Goal

Provide a repeatable checklist and script-driven checks to keep time-dependent simulations stable and defensible.

Requirements

  • Python 3.8+
  • NumPy (for matrix_condition.py and von_neumann_analyzer.py)
  • See scripts/requirements.txt for dependencies

Inputs to Gather

InputDescriptionExample
Grid spacing dxSpatial discretization0.01 m
Time step dtTemporal discretization1e-4 s
Velocity vAdvection speed1.0 m/s
Diffusivity DThermal/mass diffusivity1e-5 m²/s
Reaction rate kFirst-order rate constant100 s⁻¹
Dimensions1D, 2D, or 3D2
Scheme typeExplicit or implicitexplicit

Decision Guidance

Choosing Explicit vs Implicit

Is the problem stiff (fast + slow dynamics)?
├── YES → Use implicit or IMEX scheme
│         └── Check conditioning with matrix_condition.py
└── NO → Is CFL/Fourier satisfied with reasonable dt?
    ├── YES → Use explicit scheme (cheaper per step)
    └── NO → Consider implicit or reduce dx

Stability Limit Quick Reference

PhysicsNumberExplicit Limit (1D)Formula
AdvectionCFLC ≤ 1C = v·dt/dx
DiffusionFourierFo ≤ 0.5Fo = D·dt/dx²
ReactionReactionR ≤ 1R = k·dt

Multi-dimensional correction: For d dimensions, diffusion limit is Fo ≤ 1/(2d).

Script Outputs (JSON Fields)

ScriptKey Outputs
scripts/cfl_checker.pymetrics.cfl, metrics.fourier, recommended_dt, stable
scripts/von_neumann_analyzer.pyresults.max_amplification, results.stable
scripts/matrix_condition.pyresults.condition_number, results.is_symmetric
scripts/stiffness_detector.pyresults.stiffness_ratio, results.stiff, results.recommendation

Workflow

  1. Identify dominant physics (advection vs diffusion vs reaction)
  2. Run CFL checker with scripts/cfl_checker.py
  3. Compare to limits and adjust dt if needed
  4. Check stiffness with scripts/stiffness_detector.py if multiple scales
  5. Analyze custom schemes with scripts/von_neumann_analyzer.py
  6. Check conditioning with scripts/matrix_condition.py for implicit solves
  7. Document the stability verdict and recommended time step

Conversational Workflow Example

User: My phase-field simulation is blowing up after 100 steps. I'm using explicit Euler with dx=0.01, dt=1e-4, and diffusivity D=1e-3.

Agent workflow:

  1. Check stability criteria:
    python3 scripts/cfl_checker.py --dx 0.01 --dt 1e-4 --diffusivity 1e-3 --dimensions 2 --json
    
  2. Interpret results:
    • Fourier number: Fo = 1e-3 × 1e-4 / (0.01)² = 1.0
    • 2D limit: Fo ≤ 0.25
    • Violation: Fo = 1.0 > 0.25, unstable!
  3. Recommend fix:
    • Reduce dt to 2.5e-5 (to get Fo = 0.25)
    • Or increase dx, or switch to implicit

Pre-Simulation Stability Checklist

  • Identify dominant physics and nondimensional groups
  • Compute CFL/Fourier/Reaction numbers with cfl_checker.py
  • If explicit and limit violated, reduce dt or change scheme
  • If stiffness ratio > 1000, select implicit/stiff integrator
  • For custom schemes, verify amplification factor ≤ 1
  • Document stability reasoning with inputs and outputs

CLI Examples

# Check CFL/Fourier for 2D diffusion-advection
python3 scripts/cfl_checker.py --dx 0.1 --dt 0.01 --velocity 1.0 --diffusivity 0.1 --dimensions 2 --json

# Von Neumann analysis for custom 3-point stencil
python3 scripts/von_neumann_analyzer.py --coeffs 0.2,0.6,0.2 --dx 1.0 --nk 128 --json

# Detect stiffness from eigenvalue estimates
python3 scripts/stiffness_detector.py --eigs=-1,-1000 --json

# Check matrix conditioning for implicit system
python3 scripts/matrix_condition.py --matrix A.npy --norm 2 --json

Error Handling

ErrorCauseResolution
dx and dt must be positiveZero or negative valuesProvide valid positive numbers
No stability criteria appliedMissing velocity/diffusivityProvide at least one physics parameter
Matrix file not foundInvalid pathCheck matrix file exists
Could not compute eigenvaluesSingular or ill-formed matrixCheck matrix validity

Interpretation Guidance

ScenarioMeaningAction
stable: trueAll checked criteria satisfiedProceed with simulation
stable: falseAt least one limit violatedReduce dt or change scheme
stable: nullNo criteria could be appliedProvide more physics inputs
Stiffness ratio > 1000Problem is stiffUse implicit integrator
Condition number > 10⁶Ill-conditionedUse scaling/preconditioning

Limitations

  • Explicit schemes only for CFL/Fourier checks (implicit is unconditionally stable)
  • Von Neumann analysis assumes linear, constant-coefficient, periodic BCs
  • Stiffness detection requires eigenvalue estimates from user

References

  • references/stability_criteria.md - Decision thresholds and formulas
  • references/common_pitfalls.md - Frequent failure modes and fixes
  • references/scheme_catalog.md - Stability properties of common schemes

Version History

  • v1.1.0 (2024-12-24): Enhanced documentation, decision guidance, examples
  • v1.0.0: Initial release with 4 stability analysis scripts

Source

git clone https://github.com/HeshamFS/materials-simulation-skills/blob/main/skills/core-numerical/numerical-stability/SKILL.mdView on GitHub

Overview

This skill provides a repeatable checklist and script-driven checks to ensure numerical stability in time-dependent PDE simulations. It helps you choose dt and schemes (explicit vs implicit), diagnose instability sources, and verify CFL/Fourier criteria, von Neumann amplification, stiffness, and conditioning.

How This Skill Works

Inputs like dx, dt, v, D, k, dimensions, and scheme type feed into a suite of Python scripts (cfl_checker.py, von_neumann_analyzer.py, matrix_condition.py, stiffness_detector.py). The tools output metrics, a recommended_dt, and a stability verdict, guiding you through a structured workflow from dominant physics identification to conditioning checks and documentation.

When to Use It

  • Decide between explicit and implicit schemes for a stiff or near-stiff time-dependent PDE.
  • Verify CFL/Fourier/Reaction numbers to confirm a safe dt for advection-diffusion-reaction problems.
  • Diagnose numerical blow-up or instability in an explicit time-stepping run.
  • Assess matrix conditioning before solving implicit systems or implementing IMEX schemes.
  • Detect stiffness with multi-scale dynamics and choose appropriate stabilizing integrators.

Quick Start

  1. Step 1: Gather inputs (dx, dt, v, D, k, dimensions, scheme type) for your problem.
  2. Step 2: Run stability checks (e.g., python3 scripts/cfl_checker.py --dx <dx> --dt <dt> ... --json) and review metrics.
  3. Step 3: If unstable or stiff, adjust dt or switch schemes; re-run CFL, stiffness, and conditioning checks until stable.

Best Practices

  • Collect and standardize inputs: dx, dt, v, D, k, dimensions, and scheme type before testing.
  • Run CFL/Fourier/Reaction criteria checks and compare against theoretical limits (C ≤ 1, Fo ≤ 0.5 in 1D, Fo ≤ 1/(2d) multi-d).
  • If a problem is stiff, prefer implicit or IMEX schemes and assess conditioning with matrix_condition.py.
  • Use stiffness_detector to flag high stiffness ratios and justify scheme changes.
  • Document the stability verdict, inputs, outputs, and recommended dt to enable reproducibility.

Example Use Cases

  • 2D advection-diffusion with explicit Euler blows up; stability checks reveal Fo > 0.25, prompting dt reduction.
  • Phase-field simulation switches from explicit to implicit after stiffness is detected.
  • Explicit scheme passes CFL checks in 1D but fails in 2D; multi-dimensional diffusion requires Fo ≤ 1/(2d).
  • IMEX approach is adopted after conditioning looks favorable and stiffness is moderate.
  • Custom finite-difference scheme analyzed with von Neumann to validate amplification factor ≤ 1.

Frequently Asked Questions

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