学术报告

学术活动

学术报告
05/08 2026 Seminar
  • Title题目 A New Picture of Lattice QFT: from Category Theoretic Framework to Machine Learning Renormalization
  • Speaker报告人 陈静远/Jing-Yuan Chen (清华大学)
  • Date日期 2026年5月8日 10:00
  • Venue地点 南楼6620
  • Abstract摘要

    I will introduce a new framework to understand what a lattice QFT “really is” in relation to continuum QFT, by elevating the physically intuitive idea behind the Villain model into the mathematical language of higher category theory. Long standing problems in the lattice community on unambiguously defining topological operators on the lattice---most notably instanton density in lattice QCD---are solved by refining the traditional models using this categorical language, in a natural and necessary manner. (Yes, this means category theory is "crucially useful" to traditional problems in physics.) 

    The next stage is to implement such refined models into actual numerics. Real space numerical renormalization, aided with some simple machine learning, fits naturally into our categorical framework, and provides a nice solution to some challenging technicalities in the implementation. I will show our preliminary numerical results on the refined CP^N model, as a prototypical study for the refined Yang-Mills theory.

    Reference: 2406.06673 and on-going works

    Biography

    Jing-Yuan Chen is a Member at the Institute for Advanced Study at Tsinghua University. He obtained B.Sc. in Physics and Mathematics at the University of Michigan in 2011, and Ph.D. in Physics, supervised by Dam Thanh Son, at the University of Chicago in 2016. After that, he was a Gordon and Betty Moore Postdoctoral Fellow at the Stanford Institute for Theoretical Physics, until joining the current position in 2020. 

    Inviter: Gang Yang



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