Orbithunter v 1.1.1: Framework for Nonlinear Dynamics and Chaos

Orbithunter serves as a framework for solving chaotic nonlinear partial differential equations via variational formulation. In other words, equations are posed as boundary value problems (typically with periodic boundary conditions) which manifest as differential algebraic equations.

This is in stark contrast with typical dynamical systems formulation of chaotic systems, which use an initial value problem in time. The argument in favor of orbithunter’s BVP/DAE formulation is that by definition, the hyperbolic dynamical systems under investigation suffer from exponential instabilities. This relegates forecasting and prediction to a finite time window. Orbithunter believes that this is a clear indication that posing the problem as an initial value problem is incorrect; one must create a setting where dynamics no longer take place.

Want a crash course on chaos or a good reference text (not https secure)? Alternatively, if you would like a more traditional approach to computational chaos in fluid dynamics, check out openpipeflow or channelflow 2.0. If you’re looking ahead to the future, the next big thing might be based in Julia.


  • An object oriented approach of differential algebraic equations.

  • Vectorized (tensorized really) computations using NumPy broadcasting and tensor operations.

  • A general-purpose framework for finding, visualizing and manipulating these solutions

  • High-level access to SciPy API for usage with differential algebraic equations.

  • New spatiotemporal techniques developed in PhD thesis

Orbithunter uses [NumPy] and [SciPy] for its numerical calculations. Its design emphasizes user-friendliness and modularity; giving quick and easy access to high-level numerical operations.

Checkout the resources included in the github repository for more help and tutorials!

Special Thanks

I’d like to thank the open source projects [Networkx], [NumPy], [SciPy] and many other packages for being great guides of how to setup, document, and structure Python packages, in addition to the great tools they provide.

Thank you to Predrag Cvitanovic for his courses and guidance throughout my PhD thesis, Burak Budanur for being a great source of information and friend, John Gibson, Ashley Willis and many others.