\u3010Date\u3011
10:00-11:30 am JST, December 20<\/p>\n
\u3010Speaker\u3011
Akio Tomiya \/IPUT<\/p>\n
\u3010\u30bf\u30a4\u30c8\u30eb\/Title\u3011
\"Lattice QCD and Machine learning\u201d<\/p>\n
\u3010Abstract\u3011
In this talk, I will explain recent progress in theoretical physics, specifically in lattice QCD and its use of machine learning. I will not assume that the audience has expert particle physics or machine learning knowledge. Particle physics is a field that seeks to understand the microscopic world and the history of the universe using relativistic quantum theory. Lattice QCD, in particular, is a way of describing the fundamental structure of the nucleus. However, the equations in this theory are difficult to solve, so we rely on supercomputers and an algorithm called Markov chain Monte Carlo. While these computers and the algorithm are very powerful but still face challenges. We use machine learning to overcome such difficulties. Machine learning and deep learning have been used extensively in various fields, including engineering, industry, and the natural sciences. They are also being actively studied for use in theoretical physics, opening up new possibilities in this field. In this talk, I will introduce lattice QCD and discuss the use of machine learning in this area.<\/p>\n