Open and FAIR Fusion for Machine Learning Applications

Multi-institutional collaboration towards a Fusion Data Platform for ML applications

   Cristina Rea, Gregorio L. Trevisan    •     Grants

The project “Open and FAIR Fusion for Machine Learning Applications” is a multi-institutional collaboration to develop a Fusion Data Platform for Machine Learning applications using Magnetic Fusion Energy (MFE) data.

MFE devices participating in this research are Alcator C-Mod, Pegasus-III, CTH and HBT-EP. An interoperable and publicly available library will be developed leveraging data from these devices. The library will have built-in pipelines for ML application design, allowing preservation of reproducible scientific results. Curated research products will be released through the newly designed platform, which will adhere to Findable, Accessible, Interoperable, Reusable (FAIR) and Open Science (OS) guidelines.

Institutional PIs and Collaborating Institutions under the DOE grant.

W&M Summer School

An intensive 2-week summer school focused on undergraduate students with backgrounds in physics, engineering, computer science, applied mathematics and data science will be offered at William & Mary. This summer course will include a close to equal distribution of traditional instruction and active projects. The traditional instruction will provide daily 50 min instruction in 4 classes with a focus on computing, applied mathematics, machine learning and fusion energy. These classes will be based on existing classes offered in data science at W&M, such as databases, applied machine learning, Bayesian reasoning in data science. These classes will be supplemented with a class focused on fusion energy for the applications the students will tackle during the hands-on component and for students’ summer research.

The 1st edition of the Summer School will be held in person at W&M during June 3-15, 2024.

For registration and contact information, please refer to the official Project website.

External collaborators

  • J. Levesque, Columbia University.
  • N. Cummings, UKAEA.
  • N. Murphy, Center for Astrophysics - Harvard & Smithsonian.
  • A. Pau, EPFL SPC.

and with the support of the International Atomic Energy Agency (IAEA).

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