Aug 2 – 6, 2021
Europe/Brussels timezone

Machine learning for Lattice QCD

Aug 6, 2021, 4:20 PM


Parallel contribution H. Statistical Methods for Physics Analysis in the XXI Century Parallels Track H


Dr Tomiya Akio (Riken/BNL)


In this talk, we introduce machine learning techniques for lattice QCD. Lattice QCD is one of the most successful methodologies of quantum field theory, which provides us quantitative values of QCD. On the other hand, machine learning enables us to treat big structured data. In particular, neural networks are widely used since it has universal approximation property while it cannot be exact. It seems that machine learning with lattice QCD is not match. We discuss exact ways to use machine learning in lattice QCD. Especially, we will introduce treatment of gauge symmetry in the neural network.

Primary author

Dr Tomiya Akio (Riken/BNL)

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