Lorenz System Solver

Explore chaotic flow through adjustable Lorenz parameters. Run Euler or RK4 steps with custom timestep. View phase behavior, then download tables in seconds easily.

Inputs

Prandtl-like coupling strength.
Thermal forcing parameter.
Geometric dissipation factor.
Smaller dt improves stability.
Up to 200,000 steps.
Save every N steps to the table.
RK4 is robust for chaotic flows.
Results appear above after submission.

Formula used

The Lorenz system is a three-variable nonlinear model often used to study chaos:

dx/dt = σ (y − x)
dy/dt = x (ρ − z) − y
dz/dt = x y − β z

This solver advances the state over time using Euler or fourth‑order Runge–Kutta (RK4) integration.

How to use this calculator

  1. Set σ, ρ, and β to match your scenario.
  2. Choose initial values x₀, y₀, z₀ for the starting state.
  3. Select dt and total steps; smaller dt is usually safer.
  4. Pick RK4 for accuracy or Euler for quick experiments.
  5. Adjust output stride to control the table size.
  6. Submit to view the trajectory and download CSV/PDF.

Example data table

σρβx₀y₀z₀dtstepsmethod
10282.66671110.015000RK4
14353.00000.1000.00512000RK4
10242.66672220.014000Euler
These are typical input sets used for testing trajectories.

Article

1) Purpose of the Lorenz solver

The Lorenz system is a compact model that produces rich, chaotic motion from three coupled ordinary differential equations. This calculator numerically integrates the state over time so you can study sensitivity to initial conditions, parameter tuning, and long‑run bounded behavior using reproducible settings.

2) The governing equations you are integrating

The solver advances x(t), y(t), and z(t) under the standard Lorenz flow. With σ controlling coupling, ρ acting as forcing, and β setting dissipation, small changes can shift the trajectory from steady convergence to oscillations and chaotic wandering.

3) Common reference parameters and what they show

A widely used benchmark set is σ = 10, ρ = 28, and β = 8/3. With modest initial values such as (1, 1, 1), many simulations exhibit the classic “butterfly” attractor. Reducing ρ toward the low‑20s often weakens chaotic structure, while increasing ρ can enlarge the explored range.

4) Integration method: Euler versus RK4

Euler integration is fast and simple, but it can accumulate error quickly in nonlinear chaotic systems. Fourth‑order Runge–Kutta (RK4) evaluates intermediate slopes to improve accuracy per step. For comparable settings, RK4 typically preserves bounded dynamics with fewer artifacts at practical time steps.

5) Choosing dt for stability and fidelity

The time step dt is the most important numerical control. Smaller dt reduces truncation error and improves stability, but increases runtime. Values around 0.01 are common for exploratory runs; reducing to 0.005 or 0.001 can help when you increase steps or push parameters into more extreme regimes.

6) Steps, stride, and data volume management

Total steps define the simulation horizon T = steps × dt. Because saving every step can create very large tables, the output stride stores one row every N steps. For example, 50,000 steps with stride 10 yields about 5,001 saved rows, balancing detail with lightweight exports.

7) Reading results: final state and observed ranges

The results panel reports the final state and the observed minima and maxima for x, y, and z over the run. These ranges are useful quick diagnostics: exploding ranges can indicate an unstable dt or method choice, while tight ranges may reflect convergence or a parameter set outside chaotic windows.

8) Export-ready workflow for analysis

After you submit inputs, the trajectory table appears immediately above the form, ready for review. Use the CSV export to load data into spreadsheets or scientific tools, and use the PDF export for reporting. Keeping stride consistent makes comparisons across multiple parameter sweeps straightforward.

FAQs

1) What does this solver compute?

It numerically integrates the Lorenz differential equations to produce a time‑ordered trajectory of (t, x, y, z) using your parameters, initial conditions, time step, and selected method.

2) Which method should I choose for most runs?

RK4 is recommended for accuracy and better stability at practical dt values. Euler can be useful for quick demonstrations, but it may distort chaotic dynamics unless dt is very small.

3) Why does changing dt change my trajectory?

dt controls truncation error. In chaotic systems, small numerical differences grow rapidly, so trajectories diverge over time. Smaller dt generally improves fidelity and reduces instability artifacts.

4) What are typical parameter values to start with?

A common starting set is σ=10, ρ=28, β=8/3 with initial (1,1,1). This often produces the classic attractor shape while keeping the run stable with dt around 0.01.

5) How do I reduce file size without losing behavior?

Increase output stride to save fewer rows while keeping the same dt and steps. You’ll still capture overall structure, and exports remain fast and manageable.

6) Why do I see exploding values or strange ranges?

This can happen when dt is too large, Euler is used at aggressive settings, or parameters push the system numerically unstable. Try RK4, reduce dt, and verify your parameter magnitudes.

7) Can I compare two runs fairly?

Yes. Keep dt, steps, and stride consistent, then vary one factor such as ρ or initial conditions. Use the CSV export to overlay trajectories or compute statistics across runs.

Related Calculators

shannon entropy calculatorphase locking valuekaplan yorke dimensionfalse nearest neighborspoincaré recurrence timeaverage mutual informationbifurcation diagram generatortime series surrogatelogistic map bifurcationlyapunov spectrum calculator

Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.