Name: Chaotic Attractors with Boost.OdeInt
Start: 2025-10-08T18:00:00-06:00
End: 2025-10-08T20:00:00-06:00

Chaotic dynamical systems are modeled by evolving system state through a series of differential equations. A dynamical system is considered chaotic if small changes in the initial conditions result in wildly different final conditions. A famous chaotic dynamical system is the [Lorenz system](https://en.wikipedia.org/wiki/Lorenz_system) of equations that were created to model weather patterns. Other examples of chaotic dynamical systems are the [Rossler attractor](https://en.wikipedia.org/wiki/R%C3%B6ssler_attractor) and the [Van der Pol oscillator](https://en.wikipedia.org/wiki/Van_der_Pol_oscillator).

Exploring these systems takes you down the mathematical rabbit hole of numerical integration. The classic reference "[Numerical Recipes](https://en.wikipedia.org/wiki/Numerical_Recipes)" gives algorithms and their associated mathematical analysis for many problems, including numerical integration. Getting the details right can be tricky and if you're not experienced in the underlying mathematics, it's easy to make mistakes.

We can get a variety of numerical integration algorithms, each with their own trade-offs, by using the [Odeint library](https://www.boost.org/doc/libs/latest/libs/numeric/odeint/doc/html/index.html) from [Boost](https://boost.org). Odeint means "Ordinary Differential Equation Integration" and is a library for solving [initial value problems](https://en.wikipedia.org/wiki/Initial_value_problem) of [ordinary differential equations](https://en.wikipedia.org/wiki/Ordinary_differential_equation). An initial value problem means we know the starting state of the system and we perform numerical integration of the equations to learn the subsequent state of the system. Ordinary differential equation means that the underlying equations depend on only a single variable, which is time in our case.

This month, Richard Thomson will give us an introduction to Boost.Odeint and use it to plot out the evolving state of different chaotical dynamical systems. We'll look at how Odeint can be used with different data structures for representing the state of our dynamical system. We'll see how well Odeint can be used on the GPU to get faster evaluation of our system.

This will be an online meeting, so drinks and snacks are on you!

Join the meeting here: [https://meet.xmission.com/Utah-Cpp-Programmers](https://meet.xmission.com/Utah-Cpp-Programmers)

Watch previous topics on the Utah C++ Programmers YouTube channel:
[https://www.youtube.com/@UtahCppProgrammers](https://www.youtube.com/@UtahCppProgrammers)

Future topics: [https://utahcpp.wordpress.com/future-meeting-topics/](https://utahcpp.wordpress.com/future-meeting-topics/)
Past topics: [https://utahcpp.wordpress.com/past-meeting-topics/](https://utahcpp.wordpress.com/past-meeting-topics/)

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Chaotic Attractors with Boost.OdeInt

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Chaotic Attractors with Boost.OdeInt

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