Home¶
The FAIR-HEP Project¶
Findable, Accessible, Interoperable, and Reusable Frameworks for Physics-Inspired Artificial Intelligence in High Energy Physics.
The primary focus of this project is to advance our understanding of the relationship between data and artificial intelligence (AI) models by exploring relationships among them through the development of FAIR (Findable, Accessible, Interoperable, and Reusable) frameworks. Using high-energy physics (HEP) as the science driver, this project will develop a FAIR framework to advance our understanding of AI, provide new insights to apply AI techniques, and provide an environment where novel approaches to AI can be explored.
Get the Code¶
The source code of FAIR-UMN-CDMS is available in the CDMS Repository.
The source code of FAIR-UMN-ECAL is available in the ECAL Repository.
Contact¶
raise issues in the CDMS Repository & ECAL Repository or
send an email to:
Bhargav Joshi (joshib@umn.edu)
Buyun Liang (liang664@umn.edu)
Roger Rusack (rusack@umn.edu)
Ju Sun (jusun@umn.edu)
Also, we are always looking for contributors and collaborators.