Suggestion: Bookmark this page for easy access to all infos you need for the course.
Each lecture contains material on physics, numerics, technical concepts, as well as exercises. The lecture content is outlined in its introduction using the following items for each type of content:
📚 Physics: equations, discretisation, implementation, solver, visualisation
💻 Code: technical, Julia, GitHub
🚧 Exercises
The course will be taught in a hands-on fashion, putting emphasis on you writing code and completing exercises; lecturing will be kept at a minimum.
Online attendance will be possible on Zoom for ETH students only
No online support will be provided during the exercise session, please follow the lectures
Schedule will be defined and communicated on the last lecture.
We use Element as the main channel for communication between the teachers and the students, and hopefully also between students. We encourage ETH students to ask and answer questions related to the course, exercises and projects there.
Head to the Element chat link on Moodle to get started with Element:
Select Start Student-Chat
Login using your NETHZ credentials to start using the browser-based client
Join the General and Helpdesk rooms
Download the desktop or mobile client for more convenient access or in case of encryption-related issues
Homework tasks will be announced after each week's lecture. The exercise session following the lecture will get you started.
Homework due date will be Wednesday 23h59 CET every following week (8 days) to allow for Q&A during the following in-class exercise session.
All homework assignments can be carried out by groups of two. However, note that every student has to hand in a personal version of the homework.
➡ Check out the Homework page for an overview on expected hand-in and deadlines.
Submission of JupyterHub notebooks after weeks 1 and 2, then GitHub commit hash (SHA) after week 3 and onwards, or other documents happens on the course's Moodle.
Actions and tasks related to GitHub will happen on your private course-related GitHub repository.
Starting from lecture 3 and onwards, the development of homework scripts happens on GitHub and you will have to submit the git commit hash (SHA) on Moodle in the related git commit hash (SHA) submission activity.
on the Hub place all notebooks of an assignment into one folder called assignments/lectureX_homework
note: maybe this folder magically already exists on your Hub with the notebooks added. If not, create it and download the notebooks yourself.
in Moodle during submission, select that folder as JupyterHub submission
Once you have your GitHub account ready (see lecture 2 how-to), create a private repository you will share with the teaching staff only to upload your weekly assignments:
Create a private GitHub repository named pde-on-gpu-<moodleprofilename>, where <moodleprofilename> has to be replaced by your name as displayed on Moodle, lowercase, diacritics removed, spacing replaced with hyphens (-). For example, if your Moodle profile name is "Joël Désirée van der Linde" your repository should be named pde-on-gpu-joel-desiree-van-der-linde.
Select an MIT License and add a README.md file.
Share this private repository on GitHub with the teaching bot.
For each homework submission, you will:
create a git branch named homework-X (X ) and switch to that branch (git switch -c homework-X);
create a new folder named homework-X to put the exercise codes into;
(don't forget to git add the code-files and git commit them);
push to GitHub and open a pull request (PR) on the main branch on GitHub;
copy the single git commit hash (SHA) after the final push and the link to the PR and submit both on Moodle as the assignment hand-in (it will serve to control the material was pushed on time);
(do not merge the PR yet).
homework-X folders and README.md in the GitHub repo you share with the exercise bot in order to keep the repository lightweight.Project.toml file. The Manifest.toml file should be excluded from version control. To do so, add it as entry to a .gitignore file in the root of your repo. Mac users may also add .DS_Store to their global .gitignore. Codes could be placed in a scripts/ folder. Output material to be displayed in the README.md could be placed in a docs/ folder.After the submission deadline, we will correct and grade your assignments. You will get personal feedback directly on the PR as well as on Moodle. Once you got feedback, please merge the PR.
We will try to correct your assignments before the lecture following the homework's deadline. This should allow you to get rapid feedback in order to clarify the points you may struggle on as soon as possible.
Starting from lecture 7, and until lecture 9, homework assigments contribute to the course's first project. The goal of this project is to have a multi-xPU thermal porous convection solver in 3D.
The exercises in lecture 7 will serve as starting point for the first project:
Within your pde-on-gpu-<moodleprofilename> folder, copy over the PorousConvection you can find in the l7_project_template folder within the scripts folder. Make sure to copy the entire folder as not to loose the hidden files.
Follow the specific instructions given in Lecture 7 - infos about projects.
During lectures 7 and 9 you will be asked to add material to the PorousConvection folder as part of regular homework hand-in which will serve as evaluation for the Part 2 (35% of the final grade) (see Evaluation section).
The project submission deadline is set to 28.11.2024 - 23h59 CET (see also Homework). The final GitHub SHA has to be added to Moodle in the Lecture 9 section.
Make sure to have following items in your private GitHub repository:
a PorousConvection folder containing the structure proposed in Lecture 7
the 2D and 3D scripts from Lecture 7
the CI set-up to test the 2D and 3D porous convection scripts
a lecture_8 folder (different from the PorousConvection folder) containing the codes, README.md and material listed in Exercises - Lecture 8
the 3D multi-xPU thermal porous convection script and output as per directions from Exercises - Lecture 9.
In addition enhance the README.md within the PorousConvection folder to include:
a short motivation/introduction
concise information about the equations you are solving
concise information about the numerical method and implementation
the results, incl. figures with labels, captions, etc...
a short discussion/conclusion section about the performed work, results, and outlook
Note that for evaluation will be considered the following (non-exhaustive) items: code correctness, style, and conciseness; implementation of demanded tasks; final layout and rendering, ...
For information about topics for the final project, head to Information about final projects page.
The following steps will get you started with the final projects:
Find a classmate to work with (being your own mate is fine too)
Select a topic of your choice
Initiate a private GitHub repository for your project (CamelCaps, including .jl at the end - e.g.: MyProject.jl)
Share the final project private repository on GitHub with the teaching-bot (https://github.com/teaching-bot)
Send and email to Ivan (iutkin@ethz.ch) and Ludovic (luraess@ethz.ch) by Tuesday December 3, 2024, with subject Final projects including
your project partner
a brief description of your choice
a link to your final project GitHub repository
anything else missing in this list
Work on your final project, asking for help
in the Element Helpdesk channel for general question
as GitHub "issue" for project specific questions
during class hours serving as helpdesk
Submission deadline for the project is December 20, 2024 – 23h59 CET.
Final submission timestamp is enforced upon tagging the v1.0.0 version release of your repository. See GitHub docs for infos.
Also, add the last commit SHA to Moodle - Final project submission as for the exercises.
Grading of the final project will contribute 35% of the final grade.
For a successful outcome, final projects are expected to be handed-in as single GitHub repository featuring the following items:
documented and polished scripts (using e.g. docstrings, in-line comments)
documentation including:
an enhanced README.md following to proposed structure with equations, cross-references, figures, figure captions
instructions to run the software and reproduce the results
references
unit and reference testing
Continuous Integration (CI - using e.g. GitHub Actions)
additional features if needed
All homework assignments can be done alone or in groups of two.
Enrolled ETHZ students will have to hand in on Moodle and GitHub:
Six weekly assignments during the course's Part 1 and Part 2 constitute 30% of the final grade. The best five out of six homework will be counted.
A project developed during Part 3 of the course constitutes 35% of the final grade
A final project constitutes 35% of the final grade
Project submission includes code in a Github repository and an automatically generated documentation.
