Learning Quantum Field Theory with Equivariant Continuous Flows.

  • Event Date: 2022-09-21
  • High-performance computation and machine learning
  • Speaker: Dr. C.N. (Miranda) Cheng (University of Amsterdam & AS)  /  Host: Prof. Ying-Jer Kao (Department of Physics, NTU)
    Place: NCTS Physics Lecture Hall, 4F, Chee-Chun Leung Cosmology Hall, NTU (Hybrid)

Title: Learning Quantum Field Theory with Equivariant Continuous Flows
Speaker: Dr. C.N. (Miranda) Cheng (University of Amsterdam & AS)
Start Date/Time: 2022-9-21 / 10:30 (Taipei time)
End Date/Time: 2022-9-21 / 12:00
Host : Prof. Ying-Jer Kao (Department of Physics, NTU)

(Hybrid)
Onsite: NCTS Physics Lecture Hall, 4F, Chee-Chun Leung Cosmology Hall, NTU 
Online Zoom link: https://us02web.zoom.us/j/83736741073?pwd=MWVkU29rTFpkcUdtYitDTGVwa3VtUT09
Online Zoom [Registration] is required

Abstract: Machine learning has the potential of becoming, among other things, a major computational tools in physics, making possible what was not. Specifically, I will summarise my recent work which aims to use a continuous flow model to help ameliorate the numerical difficulties in sampling in lattice field theories, which for instance hampers high-precision computations in LQCD. I will focus on the case study of the phi^4 theory in 2 dimensions. The talk will be based on 2110.02673 and 2207.00283 with de Haan, Gerdes, Rainone, and Bondesan.

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