C and Python examples from my book on using PETSc and Firedrake to solve PDEs

Wave propagators for seismic domains with application to full waveform inversion.

A shape optimisation toolbox built on the Firedrake finite element library.

Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".

Slides/notes and Jupyter notebook demos for an introductory course of numerical methods for PDEs

FEMlium – interactive visualization of finite element simulations on geographic maps with folium

Differentiable interface to Firedrake for JAX

Easy interoperability with Automatic Differentiation libraries through NumPy interface to Firedrake and FEniCS

Finite Element Method for Electrochemical Transport (EchemFEM)

Automatic differentiation of FEniCS and Firedrake models in Julia

Goal-oriented error estimation and mesh adaptation for finite element problems solved using Firedrake

Seismic inversion using a neural network regulariser implemented as an ExternalOperator in Firedrake

Mesh adaptation utilities for coastal ocean modelling in Firedrake and Thetis.

The firedrake-ts library provides an interface to PETSc TS for the scalable solution of DAEs arising from the discretization of time-dependent PDEs.

Physics-driven machine learning using PyTorch and Firedrake

viskex - interactive visualization for firedrake and FEniCSx

Firedrake notes

Demo and simple scripts using Firedrake

Mesh movement methods for finite element problems solved using Firedrake

Slides from my talk at the EuroSciPy conference 2014