Description

Symbolic regression can be used to rapidly provide solutions to problems in science which may have large computational complexity or may even be intractable. It can be used to discover a symbolic expression describing data such as a physical law. Many approaches have been explored but this project will focus on building a foundation model which incorporates knowledge about the task of symbolic regression and common data representations for physical systems.

Duration

Total project length: 175/350 hours.

Task ideas and expected results

• Explore physics-informed ideas for improving data representations, physics-aware models, and physics simulations for squared amplitude calculation
• Apply symbolic machine learning techniques to predict the squared amplitudes and cross section for high-energy physics

Requirements

• Python, C++ and some experience in Machine Learning sequence models.
• Some knowledge of physics and extensive knowledge of mathematics preferred.

Difficulty Level

Advanced

Mentors

• Abdulhakim Alnuqaydan (Qassim University)
• Sergei Gleyzer (University of Alabama)
• Harrison Prosper (Florida State University)
• Eric Reinhardt (University of Alabama)

Please DO NOT contact mentors directly by email. Questions should instead be directed to ml4-sci@cern.ch which is forwarded to mentors. To submit your proposal, CV, and test task solutions, please use this Google form.

Corresponding Project

• SYMBA

Participating Organizations

• Alabama
• FSU
• QU

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