Welcome to the OpenFOAM machine learning hackathon repository! The hackathon is a community event organized by the data-driven modeling special interest group. If you are an OpenFOAM user excited about combining OpenFOAM and machine learning, this event is for you!
A hackathon is an intensive get-together for creative problem solving in groups. The corner stones of the OpenFOAM-ML hackathon are as follows:
- objective: we prepare 2-3 exciting projects combining recent ML techniques and OpenFOAM; the topics are diverse and change from event to event; for each project, a starter code is provided; your task is to advance the starter code in a self-chosen direction; we provide a couple of ideas to get you started
- time limit: the hackathon consists of three full days of intense hacking
- team work: each participant chooses the preferred project/starter code; within each project, the participants are split up into groups of 2-5 people; we aim for a minimum of one advanced hackathon participant per group to provide some guidance
- workshops: for each project, a workshop introduces the starter code and a necessary minimum of theory
- hacking sessions: the groups advance their projects; we aim to provide close mentor support for all groups via gather.town and slack
- final presentation: each team presents their final results and receives feedback from the other participants and mentors
The workshop is fully virtual. There is no geographical restriction for participants, but keep in mind that we cannot accommodate all time zones. The organizers' time zone is CET.
- Nov 11, 2022: start of the application process
- Dec 31, 2022: end of the application process
- Jan 09, 2023: feedback on the applications
- Jan 23-25, 2023: 2nd hackathon
A detailed schedule will be provided at the beginning of the hackathon. Note that you should reserve three full days for the hackathon.
Since we aim to provide all participants with close support during the hackathon, the number of participants is limited to 20. Applications are accepted until Dec 31, 2022. There are no registration fees or other costs. We can also provide compute resources thanks to AWS, so you do not need any specialized hardware. Admission is not guaranteed. Based on all applications, we will select the most suitable candidates.
- Combining OpenFOAM and physics-based machine learning with Nvidia Modulus; refer to the Modulus user guide to prepare for this project
- Coupling OpenFOAM and machine learning libraries via SmartSim; the goal of this project is to perform online inference and data extraction during the simulation; the test case is based on this work; moreover, this example provides some guidance for the OpenFOAM-SmartSim coupling
- learning and monitoring closed-loop flow control strategies with drlFoam; we'll apply deep reinforcement learning to new test cases and build a monitoring dashboard with dash; to learn about closed-loop control with DRL, refer to this article
Questions about the event? Get in touch by opening a new issue in this repository or contact the chairs of the data-driven modeling SIG.
This event is generously supported by our sponsors.
| AWS | ESI | Nvidia |
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