A selection of scientific computing projects that I maintain on GitHub.

A selection of scientific computing projects that I maintain on GitHub.

An implementation of Trefethen and Battles' Chebfun package in Python.

Using the de Casteljau algorithm to create $C^2$ splines on symmetric spaces.

Minimise any loss function using automatic differentiation to do direct or indirect matching

Python library for computing shape invariants of planar curves using finite element method and the FEniCS package.

Bifurcation diagrams of traveling waves for nonlinear wave equations.

Simulations of spin systems with the spherical midpoint method.

Simulations of wave maps with the multi-symplectic SHAKE method.

Integrate any differential equation on any homogeneous space. It is an implementation of the skeleton description of Runge–Kutta methods on homogeneous spaces. The only requirement is to have an *isotropy choice* at your disposal for the homogeneous space at hand. Many such isotropy choices are available from our paper. There are examples in the corresponding notebook demo.

Padé approximations of exponential (φ) functions, a port of the expint Matlab package.

Numerical simulation of ODEs in Python