Tag: ODE

Estimating Lyapunov Spectra of ODEs using Python
Python code is shown that estimates the Lyapunov spectra for the Rossler and Lorenz systems. This code is written in a way that makes it adaptable for other continuoustime systems.

Generating Poincaré Sections and Return Maps
Using the iterative 4th order RungeKutta method as described here, we can create low dimensional slices of the system’s attractor known as Poincare Sections, Return maps, or Recurrence maps. We will use the Rossler attractor for this example, with a, b, and c set to 0.2, 0.2, and 5.7, respectively. Poincare sections are important for visualizing…

Generating time series for Ordinary Differential Equations
We can produce a time series from ordinary differential equations by solving the equations using the iterative 4th order RungeKutta method and plugging each of the solutions back into the equations.