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Add version references to examples page, and update readme
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kburns committed Feb 14, 2022
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92 changes: 88 additions & 4 deletions README.md
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<img alt="PyPI - License" src="https://img.shields.io/pypi/l/dedalus">
</p>

**Note: this branch is the development head of v3 of the codebase, which is currently under beta-release.
The development head of v2 of the codebase is on the ["v2_master" branch](https://github.com/DedalusProject/dedalus/tree/v2_master).**

Dedalus is a flexible framework for solving partial differential equations using spectral methods.
The code is open-source and developed by a team of researchers studying astrophysical and geophysical fluid dynamics.
The code is open-source and developed by a team of researchers studying astrophysical, geophysical, and biological fluid dynamics.

Dedalus is written primarily in Python and features an easy-to-use interface with symbolic vectorial equation entry.
For example, to simulate incompressible hydrodynamics in a disk or ball, you can symbolically specify the PDEs, including boundary conditions and gauge constraints as well as the [tau modifications](https://dedalus-project.readthedocs.io/en/latest/pages/tau_method.html) needed to enforce them, as:

```python
problem.add_equation("div(u) + tau_c = 0")
problem.add_equation("dt(u) - nu*lap(u) + grad(p) + lift(tau_1) = - dot(u,grad(u))")
problem.add_equation("u(r=1) = 0")
problem.add_equation("integ(p) = 0")
```

Our numerical algorithms produce sparse and spectrally accurate discretiations of a wide variety of equations and domains, including Cartesian domains, disks, annuli, spheres, spherical shells, and balls, as seen in the following examples:

<table>
<tr style="background-color:#FFFFFF;">
<th width="25%">
<a href="https://dedalus-project.readthedocs.io/en/latest/pages/examples/ivp_1d_kdv_burgers.html">
<figure>
<img src="docs/pages/examples/images/kdv_burgers_preview.png">
<figcaption>KdV-Burgers equation (1D IVP)</figcaption>
</figure>
</a>
</th>
<th width="25%">
<a href="https://dedalus-project.readthedocs.io/en/latest/pages/examples/ivp_2d_rayleigh_benard.html">
<figure>
<img src="docs/pages/examples/images/rayleigh_benard.png">
<figcaption>Rayleigh-Benard convection (2D IVP)</figcaption>
</figure>
</a>
</th>
<th width="25%">
<a href="https://dedalus-project.readthedocs.io/en/latest/pages/examples/ivp_2d_shear_flow.html">
<figure>
<img src="docs/pages/examples/images/shear_flow.png">
<figcaption>Periodic shear flow (2D IVP)</figcaption>
</figure>
</a>
</th>
<th width="25%">
<a href="https://dedalus-project.readthedocs.io/en/latest/pages/examples/lbvp_2d_poisson.html">
<figure>
<img src="docs/pages/examples/images/poisson_preview.png">
<figcaption>Poisson equation (2D LBVP)</figcaption>
</figure>
</a>
</th>
</tr>
<tr style="background-color:#FFFFFF;">
<th width="25%">
<a href="https://dedalus-project.readthedocs.io/en/latest/pages/examples/ivp_disk_libration.html">
<figure>
<img src="docs/pages/examples/images/libration.png">
<figcaption>Librational instability (disk IVP)</figcaption>
</figure>
</a>
</th>
<th width="25%">
<a href="https://dedalus-project.readthedocs.io/en/latest/pages/examples/ivp_sphere_shallow_water.html">
<figure>
<img src="docs/pages/examples/images/shallow_water.png">
<figcaption>Spherical shallow water (sphere IVP)</figcaption>
</figure>
</a>
</th>
<th width="25%">
<a href="https://dedalus-project.readthedocs.io/en/latest/pages/examples/ivp_shell_convection.html">
<figure>
<img src="docs/pages/examples/images/shell_convection.png">
<figcaption>Spherical shell convection (shell IVP)</figcaption>
</figure>
</a>
</th>
<th width="25%">
<a href="https://dedalus-project.readthedocs.io/en/latest/pages/examples/ivp_ball_internally_heated_convection.html">
<figure>
<img src="docs/pages/examples/images/internally_heated_convection.png">
<figcaption>Internally heated convection (ball IVP)</figcaption>
</figure>
</a>
</th>
</tr>
</table>

Dedalus is written primarily in Python and features an easy-to-use interface with symbolic equation entry.
Our numerical algorithm produces sparse systems for a wide variety of equations and spectrally-discretized domains.
These systems are efficiently solved using compiled libraries and are automatically parallelized using MPI.
The resulting systems are efficiently solved using compiled libraries and are automatically parallelized using MPI.

## Links

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4 changes: 4 additions & 0 deletions docs/pages/tutorials.rst
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Tutorials \& Examples
*********************

**Note: please check you're referencing the intended version of the documentation.
This documentation is in reference to v3 of the code, which is currently under beta-release.
Documentation for v2 (the latest on PyPI) can be accessed through the sidebar.**

Tutorial Notebooks
==================

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