Examples and tutorials are provided in the form of single Python files, as Jupyter notebooks, or as mini-apps built on top of Devito.

Jupyter notebooks are files with extension .ipynb. To execute these, run jupyter notebook, and then click on the desired notebook in the window that pops up in your browser. In alternative, you may explore the pre-rendered notebooks directly on GitHub or, for a potentially smoother experience, with nbviewer.

We recommend newcomers to start with the following sets of tutorials:

• userapi: Gentle introduction to symbolic computation with Devito.
• cfd: A series of introductory notebooks showing how to use Devito to implement finite difference operators typical of computational fluid dynamics. These are based on the excellent blog "CFD Python:12 steps to Navier-Stokes" by the Lorena A. Barba group.

A set of more advanced examples are available in seismic:

• seismic/tutorials: A series of Jupyter notebooks of incremental complexity, showing a variety of Devito features in the context of seismic inversion operators. Among the discussed features are custom stencils, staggered grids, tensor notation, and time blocking.
• seismic/acoustic: Example implementations of isotropic acoustic forward, adjoint, gradient and born operators, suitable for full-waveform inversion methods (FWI).
• seismic/tti: Example implementations of several anisotropic acoustic forward operators (TTI).
• seismic/elastic: Example implementation of an isotropic elastic forward operator. elastic, unlike acoustic and tti, fully exploits the tensorial nature of the Devito symbolic language.
• seismic/viscoelastic: Example implementation of an isotropic viscoelastic forward operator. Like elastic, viscoelastic exploits tensor functions for a neat and compact representation of the discretized partial differential equations.
• seismic/self-adjoint: Self-adjoint energy conserving pseudo-acoustic operators, including notebooks for implementation of the nonlinear forward, the forward and adjoint linearized Jacobian, and tests proving accuracy and correctness.

Further:

• mpi: Jupyter notebooks explaining how MPI works in Devito.
• finance: Jupyter notebooks with examples of applying Devito to partial differential equations with financial applications.
• misc: Example operators outside the context of finite differences and
• performance: Jupyter notebooks explaining the optimizations applied by Devito, the options available to steer the optimization process, how to run on GPUs, and much more.

For developers:

• compiler: A set of notebooks exploring the architecture of the Devito compiler. This is still in its infancy.

• Articles, presentations, posters and much more concerning Devito is available here. The entries are ordered chronologically -- those at the top being the most recent ones, for each section.
• The user documentation is available here.