Machine Learning approaches in Lattice QCD - An interdisciplinary exchange

Session

ML for phase transitions and sign problem mitigation

talks and panel discussion

27 Feb 2023, 16:30

Institute for Advanced Study of the Technische Universität München

Conveners

ML for phase transitions and sign problem mitigation: Contour deformation methods for noise reduction

• Will Detmold

ML for phase transitions and sign problem mitigation

• Maria-Paola Lombardo

Description

Talks and panel discussion

77. Signal-to-noise improvement with contour deformations

Gurtej Kanwar

27/02/2023, 16:30

76. Complex normalizing flows and subtractions for sign problems

Yukari Yamauchi

27/02/2023, 16:50

78. Applying Complex Valued Neural Networks to the Hubbard Model Sign Problem: A Survey and Case Study

Marcel Rodekamp

27/02/2023, 17:10

79. Panel discussion: 1. maximum improvement in <sign> 2. ML towards such a maximal improvement 3. Contour deformation for specific observables 4. Translation between observables/theories

Chair: Will Detmold, Members: Gurtej Kanwar, Andrei Alexandru (The George Washington University), Thomas Luu, Yukari Yamauchi

27/02/2023, 17:30

1) Is there a way to determine the maximum improvement in <sign> or in S-to-N that is achievable through contour deformations and related
approaches?

2) How can ML be targeted towards such a maximal improvement?

3) Are there features of specific observables that make contour
deformation particularly effective or ineffective?

4) How do contour deformations for one observable/theory...

87. Machine learning for quantum field theories with a sign problem

Andrei Alexandru

28/02/2023, 14:30

88. A machine learning approach to the classification of phase transitions in many flavor QCD

Marius Neumann

28/02/2023, 14:50

89. Teaching how to teach: defining learning samples to detect phase transitions

Francesco Di Renzo

28/02/2023, 15:10

90. Panel discussion: 1. Competitivity of ML for Phase identification 2. ML for Hypothesised Phases 3. Potential of ML for sign porblem

Chair: Maria Paola Lombardo

28/02/2023, 15:30

Panel discussion addressing the three main questions:

--Where do we stand with the identification of phases with ML?
Do you feel ML could become competitive with standard techniques for the quantitative studies of known phase transitions i.e. order of the transition at large Nf, chiral limit, etc.

--Which are your views on the need/concrete possibility of identifying hypothesised, not...