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https://github.com/trixi-framework/talk-2021-introduction_to_julia_and_trixi

Introduction to Julia and Trixi, a numerical simulation framework for hyperbolic PDEs
https://github.com/trixi-framework/talk-2021-introduction_to_julia_and_trixi

adaptive-mesh-refinement amr conservation-laws discontinuous-galerkin julia jupyter-notebook numerical-simulation-framework trixi

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Introduction to Julia and Trixi, a numerical simulation framework for hyperbolic PDEs

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# Introduction to Julia and Trixi, a numerical simulation framework for hyperbolic PDEs



[![License: MIT](https://img.shields.io/badge/License-MIT-success.svg)](https://opensource.org/licenses/MIT)

[![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/trixi-framework/talk-2021-Introduction_to_Julia_and_Trixi/blob/main/Talk.ipynb)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/trixi-framework/talk-2021-Introduction_to_Julia_and_Trixi/HEAD?filepath=Talk.ipynb)



This is the companion repository for the talk



**Introduction to Julia and Trixi, a numerical simulation framework for hyperbolic PDEs**

*Hendrik Ranocha*

Applied Mathematics Seminar, University of Münster

Tuesday, 2021-04-27, 10:00 CEST

Announced [online](https://www.uni-muenster.de/FB10/Service/show_article.shtml?id=8490&brettid=53)



Here you can find the presentation in form of a [Jupyter](https://jupyter.org/)

notebook [`Talk.ipynb`](Talk.ipynb). This notebook contains a couple of additional

slides not presented in the talk. There are also some additional Trixi elixirs

(simulation setups) in the [`examples`](examples) directory.



## Abstract



[Julia](https://julialang.org) is a modern high-level programming language developed

specifically with scientific computing in mind.

[Trixi](https://github.com/trixi-framework/Trixi.jl) is a numerical simulation

framework for hyperbolic conservation laws written in Julia. A key objective for

the framework is to be useful to both scientists and students. Therefore, next

to having an extensible design with a fast implementation, Trixi is focused on

being easy to use for new or inexperienced users, including the installation

and postprocessing procedures.



This presentation is a live demonstration of Julia and Trixi. Firstly, we

introduce Julia and demonstrate some of its design principles. This introduction

is aimed at researchers in numerical analysis with previous programming experience.

Next, we show how to use Trixi for setting up and running simulations, how to

visualize the results, and how to extend Trixi with new functionality. We

demonstrate how key design principles of Julia are used in Trixi and the Julia

package ecosystem, e.g. to enable automatic differentiation through a complete

simulation involving hyperbolic conservation laws.



The presentation is available as a Jupyter notebook at

https://github.com/trixi-framework/talk-2021-Introduction_to_Julia_and_Trixi,

including information how to set up everything. For more information about Trixi

and how to use it, please visit [Trixi on GitHub](https://github.com/trixi-framework/Trixi.jl)

or refer to the [official documentation](https://trixi-framework.github.io/Trixi.jl/stable/).



## Getting started



You can view a static version of the Jupyter notebook [`Talk.ipynb`](Talk.ipynb)



- directly on GitHub (select the notebook; this may fail sometimes)

- or on [nbviewer.jupyter.org](https://nbviewer.jupyter.org/)

  (select the "render" badge at the top of this README)



These static versions do not contain output of the code cells.



### Using mybinder.org

The easiest way to get started is to click on the *Launch Binder* badge above.

This launches the notebook for interactive use in your browser without the need

to download or install anything locally.



In this case, you can skip the rest of this *Getting started* section. A

Jupyter instance will be started automagically in the cloud via

[mybinder.org](https://mybinder.org), and the notebook will loaded directly from

this repository.



*Note:*  Depending on current usage and available resources,

it typically takes 1-2 minutes to launch a notebook with

[mybinder.org](https://mybinder.org) (sometimes a little longer), so try to

remain patient. Similarly, the first two cells of the notebook take much longer

to execute than usual (around 1.5 minutes for the first Trixi simulation and

about 1 minute for the first plot), since Julia compiles all methods

"just-ahead-of-time" at first use.  Subsequent runs will be much faster.



### Setting up a local Julia/Jupyter installation

Alternatively, you can also clone this repository and open the notebook on your

local machine. This is recommended if you already have a Julia & Jupyter setup

or if you plan to try out Julia anyways.



#### Installing Julia and IJulia

To obtain Julia, go to [julialang.org/downloads](https://julialang.org/downloads)

and download the latest stable release (v1.6.0 as of 2021-04-19;

neither use the LTS release nor Julia Pro). Then, follow the

[platform-specific instructions](https://julialang.org/downloads/platform/)

to install Julia on your machine. Note that there is no need to compile anything

if you are using Linux, MacOS, or Windows.



After the installation, open a terminal and start the Julia *REPL*

(i.e., the interactive prompt) with

```shell

julia

```

To use the notebook, you also need to get the

[IJulia](https://github.com/JuliaLang/IJulia.jl) package, which provides a Julia

backend for Jupyter. In the REPL, execute

```julia

using Pkg

Pkg.add("IJulia")

```

to install IJulia. For more details, especially on how to use an existing Jupyter installation,

please refer to the [IJulia documentation](https://julialang.github.io/IJulia.jl/stable/).

From here on, we assume that you have a working installation of Julia, Jupyter,

and the Julia kernel for Jupyter.



#### Installing the required Julia packages

To make the notebook fully reproducible, we have used Julia's package manager

to pin all packages to a fixed release. This ensures that you always have a

Julia environment in which all examples in this notebook work. Later you can

always install the latest versions of Trixi and its dependencies by following

the instructions in the Trixi

[documentation](https://trixi-framework.github.io/Trixi.jl/stable/).



If you have not done it yet, clone the repository where this notebook is stored:

```shell

git clone https://github.com/trixi-framework/talk-2021-Introduction_to_Julia_and_Trixi.git

```

Then, navigate to your repository folder and open a Jupyter notebook.

```shell

cd talk-2021-Introduction_to_Julia_and_Trixi

jupyter notebook

```

The first few cells of the [Jupyter notebook](Talk.ipynb) will enable you to

download and build all required packages, including the ODE package

[OrdinaryDiffEq](https://github.com/SciML/OrdinaryDiffEq.jl), the visualization

package [Plots](https://github.com/JuliaPlots/Plots.jl), and of course

[Trixi](https://github.com/trixi-framework/Trixi.jl).

The information Julia's package manager needs for this to work is contained

in the two files [`Project.toml`](Project.toml) and [`Manifest.toml`](Manifest.toml).



As an alternative to running the examples in the notebook directly, you may

also just view the notebook *statically* by opening it within

[Jupyter NBViewer](https://nbviewer.jupyter.org/github/trixi-framework/talk-2021-Introduction_to_Julia_and_Trixi/blob/main/Talk.ipynb?flush_cache=true).



*General note:* Make sure that you execute the examples (either in the notebook

or in the REPL) *in order*, at least for the first time. Both the notebook and

the Julia REPL maintain an internal state and and some snippets depend on

earlier statements having been executed.



#### Displaying the presentation



To display the presentation as in the talk (skipping some cells/slides that

provide further information), you need the

[Jupyter extension RISE](https://rise.readthedocs.io/en/stable),

that you can install via

```shell

pip3 install --user RISE

```

After opening the Jupyter notebook, you can enter the RISE presentation mode

with `Alt + R`.



## Authors

This repository was initiated by [Hendrik Ranocha](https://ranocha.de).



## License

The contents of this repository are licensed under the MIT license

(see [LICENSE.md](LICENSE.md)).



The material in this repository is inspired by and partially derived from the talks

- [Stefan Karpinski (2019), The unreasonable effectiveness of multiple dispatch](https://www.youtube.com/watch?v=kc9HwsxE1OY)

- [Robin Deits (2020), Intro to Julia Programming Language with Detroit Tech Watch](https://www.youtube.com/watch?v=qLO-yaUkLKE)

- [Michael Schlottke-Lakemper (2021), Julia for adaptive high-order multi-physics simulations](https://github.com/trixi-framework/talk-2021-julia-adaptive-multi-physics-simulations)