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Machine learning approaches in Lattice QCD - an interdisciplinary exchange

10–13 Jun 2025
ETH Zürich
Europe/Zurich timezone

Contribution List

24 out of 24 displayed
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  1. 1. Welcome
    Marina Krstic Marinkovic (ETH Zurich)
    10/06/2025, 09:50
  2. 2. Optimal transport for anomaly detection at LHC
    Jessica N. Howard (Kavli Institute for Theoretical Physics)
    10/06/2025, 10:00
  3. 3. Machine learning RG-improved SU(3) gauge actions
    Urs Wenger (University of Bern)
    10/06/2025, 11:30

    I describe how RG-improved SU(3) gauge actions can be parametrized through machine learning gauge covariant convolutional neural networks. I discuss how the approach benefits from the straightforward accessibility of gauge field derivatives and the capability to generate targeted learning.

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  4. 4. Stochastic differentiation of Monte Carlo simulations for parameter inference in quarkonium suppression
    Tom Magorsch
    10/06/2025, 14:00

    In many scientific domains, phenomena are being described by demanding Monte Carlo simulations. A common problem setting is that these simulations depend on input parameters, whose values are a priori not clear. To determine the input parameters one usually falls back to fitting the output of the simulator to some target. This can become particularly challenging, when the simulator is...

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  5. 7. Optimizing static quark states with Neural Network parametrized general Wilson loops
    Julian Mayer-Steudte
    10/06/2025, 14:35
  6. 8. Protecting continuum symmetries on the lattice - exact prior information for reinforcement learning
    Prof. Alexander Rothkopf (Korea University)
    10/06/2025, 15:40
  7. 9. Discussion
    Nora brambilla
    10/06/2025, 16:30
  8. 10. Monte Carlo estimates of flow fields for sampling and noise problems
    Gurtej Kanwar (University of Edinburgh)
    11/06/2025, 10:00
  9. 11. Applications of flow models to the generation of correlated lattice QCD ensembles
    Fernando Romero López
    11/06/2025, 11:20

    Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. In this talk, we show examples on how these correlations can be exploited for variance reduction in the computation of observables. Different proof-of-concept applications are presented: continuum...

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  10. 12. Applications of Stochastic Normalizing Flows to gauge theories and defects
    Elia Cellini (University of Turin / INFN Turin)
    11/06/2025, 13:50

    In recent years, flow-based samplers have emerged as a promising alternative to standard sampling methods in lattice field theory. In this talk, I will introduce a class of flow-based samplers known as Stochastic Normalizing Flows (SNFs), which are hybrid algorithms combining neural networks and non-equilibrium Monte Carlo methods. I will then demonstrate that SNFs exhibit excellent scaling...

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  11. 13. Application of generative models on SU(3) gauge theories in 4 dimension
    Javad Komijani (ETH Zurich)
    11/06/2025, 14:25
  12. 14. Score-Based Diffusion Models for Lattice Gauge Theory
    Octavio Vega
    11/06/2025, 15:40
  13. 15. Discussion
    Marina Krstic Marinkovic (ETH Zurich), Prof. Sinead Ryan (Trinity College Dublin)
    11/06/2025, 16:15
  14. 16. Progress in learning contour deformations for observables
    William Detmold
    12/06/2025, 10:00
  15. 17. Diffusion models for lattice field theory
    Prof. Gert Aarts (Swansea University)
    12/06/2025, 11:30
  16. 18. Machine Learning Low-D Systems
    Prof. Thomas Luu (Forshungszentrum Jülich)
    12/06/2025, 13:50

    I discuss how machine learning is used in stochastic simulations of low-D strongly correlated systems. In particular, I show how machine learning is used to alleviate the numerical sign problem in systems that are doped and/or non-bipartite. I further discuss how flow-based generative models can be used to address ergodicity issues in low-D simulations. Finally, I argue that low-D systems...

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  17. 19. Simulations of machine-learned RG-improved SU(3) gauge action
    Kieran Holland
    12/06/2025, 14:40
  18. 26. Efficient Multilevel Sampling of Lattice Field Theory Near Criticality
    Ankur Singha
    12/06/2025, 15:45

    We present a hierarchical generative framework for efficient sampling of scalar field configurations near criticality. The method leverages a multiscale structure where coarse and intermediate fields are sampled via conditionally constructed Gaussian Mixture Models (GMMs). Normalizing Flows (NFs) refine these samples through invertible transformations that match the target distribution. This...

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  19. 20. Stochastic automatic differentiation for Lattice QCD
    Alberto Ramos Martinez (Univ. of Valencia and CSIC (ES))
    12/06/2025, 16:20
  20. 21. Discussion
    Andreas Kronfeld (Fermi National Accelerator Lab. (US))
    12/06/2025, 17:10
  21. 22. Random Matrix Theory for a nano-GPT
    Ouraman Hajizadeh (Independent Researcher)
    13/06/2025, 10:00
  22. 23. Optimized Multi-Grid Solvers: Power, Precision, and (Matrix) Products
    Evan Weinberg (NVIDIA Corporation)
    13/06/2025, 10:35
  23. 24. Recent Performance Using MILC and QUDA on Various GPU Systems
    Steven Gottlieb
    13/06/2025, 11:55

    I plan to show performance data for the MILC code calling QUDA
    for configuration generation. There will be a combination of
    benchmark runs and production runs on various systems. I will
    also show some recent results for deflation, multigrid, and
    gauge flow performed by Leon Hostetler with the assitance of Evan
    Weinberg. If there is time I will discuss some work to prepare
    for a new...

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  24. 25. Closing
    Marina Krstic Marinkovic (ETH Zurich)
    13/06/2025, 12:45