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. Current directions in symbolic regression focus either on evolutionary/genetic programming approaches or alternatively transformer based solutions. This project will explore a combination of these ideas towards a new tool for symbolic regression that can be used to solve many problems in science. As a concrete testbed for these new algorithms, the project will focus on predicting physical quantities, such as cross sections in high-energy physics, e.g a probability that a particular process takes place in the interaction of elementary particles. Its measure provides a testable link between theory and experiment. It is obtained theoretically mainly by calculating the squared amplitude.
Total project length: 175/350 hours.
Significant experience with Transformer machine learning models in Python (preferably using pytorch).
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Please DO NOT contact mentors directly by email. Instead, please email ml4-sci@cern.ch with Project Title and include your CV and test results. The mentors will then get in touch with you.