AI4EIC Community Efforts
In this webpage you can find material/documentation regarding several efforts in our community to disseminate the usage of AI/ML for the EIC. This at the moment includes lectures, outreach events, tutorials.
Community EFFORTS on AI/ML for the EIC
Lectures/Tutorials
Below are provided links to lectures, tutorials on AI/ML applications for the EIC detector.
Detector Design with AI, C. Fanelli (MIT)
design
An interactive Jupiter book presented at the NNPSS Summer School at MIT, which includes lectures and hands-on tutorials on AI-assisted design with a fully documented description of the optimization adopted during the EIC detector proposal.
Unfolding with ML : OmniFold, F. Torales Acosta (LBNL), V. Mikuni (NERSC)
Unfolding - Omnifold
- GitHub link here: https://github.com/ftoralesacosta/AI4EIC_Omnfold
- Colab link: https://colab.research.google.com/drive/1zuU9MezTIQGPhXlPG1Y9QilyDcQk6L0K?usp=sharing
- Specifically, the notebook: https://github.com/ftoralesacosta/AI4EIC_Omnfold/blob/master/DIS_Omnifold.ipynb
- Two data files on google drive that the tutorial uses:
Machine Learning Lifecycle, K. Rajput (Jefferson Lab/Data science)
ML Lifecycle
MaGraph Neural Networks, Y. (Ray) Ren
Graph Neural Networks
AI/ML examples for the EIC
Contributors
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