https://github.com/gridap/gridaptalks
Videos and slides about Gridap
https://github.com/gridap/gridaptalks
Last synced: 7 months ago
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Videos and slides about Gridap
- Host: GitHub
- URL: https://github.com/gridap/gridaptalks
- Owner: gridap
- Created: 2020-02-07T06:25:50.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-08-14T02:43:51.000Z (almost 6 years ago)
- Last Synced: 2024-12-28T03:27:33.161Z (over 1 year ago)
- Language: TeX
- Homepage:
- Size: 106 MB
- Stars: 6
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Gridap Talks
In this repos you can find some material we have recently develop for presenting the [`Gridap`](https://github.com/gridap/Gridap.jl) library.
* [July 30th, 2020] You can find [here](https://www.youtube.com/watch?v=txcb3ROQBS4) the presentation of `Gridap` in [**JuliaCon2020**](https://juliacon.org/2020/).
* [April 21st, 2020] You can also find the slides of **UNSW Online Computational Mathematics Seminar**, Melbourne-Sydney, April 21st 2020 [here](https://github.com/santiagobadia/Gridap-presentation/blob/master/UNSW-comp-math-seminar/beamer-version/sbadia-unsw.pdf), together with the video recording of the online seminar that you can download from [here](https://github.com/santiagobadia/Gridap-presentation/blob/master/unsw-video/unsw-gridap-seminar-compressed.mp4).
* [February 14th, 2020] You can find the slides of the **Monash Workshop on Numerical Differential Equations with Applications**, Melbourne, February 14th 2020, [here](https://github.com/santiagobadia/Gridap-presentation/blob/master/MWNDEA-Melbourne/beamer-version/sbadia-mwndea.pdf).
# Jupyter Notebooks
* [April 21st, 2020] You can also find a jupyter [notebook](https://github.com/santiagobadia/Gridap-presentation/blob/master/lazy-matrix-notebook/julia-basics.ipynb) in which we have created a very simple example of a _lazy ummutable matrix_ implementation in `Julia`, which we consider provides a nice overview of the `Julia` capabilities, its multiple dispatching paradigm, the importance of interfaces and _group by actions, not attributes_. we think it can be useful for people that come from object-oriented backgrounds. This example also provides some insight about how we have been able to implement our numerical PDE solvers in `Gridap`, as [@santiagobadia](https://github.com/santiagobadia) explains in the video.
## Installation of notebooks
To install the jupyter notebooks, do the following
```
$ git clone https://github.com/gridap/Tutorials.git
```
Move into the folder and open a `Julia` REPL setting the current folder as the project environment
```
$ cd Gridap-presentation/lazy-matrix-notebook/
$ julia
```
In the Julia REPL
```
julia>
```
type `]` to enter in pkg mode, activate the project
```
(@v1.4) pkg> activate .
```
and instantiate the environment
```
(lazy-matrix-notebook) pkg> instantiate
```
Open the notebook using these commands
```
julia> using IJulia
julia> notebook(dir=pwd())
```
Enjoy!