Machine learning approaches in Lattice QCD - an interdisciplinary exchange

Speaker List

• Gert Aarts (Swansea University)
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    Diffusion models for lattice field theory

• Nora brambilla
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    Discussion

• Elia Cellini (University of Turin / INFN Turin)
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    Applications of Stochastic Normalizing Flows to gauge theories and defects

• William Detmold
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    Progress in learning contour deformations for observables

• Steven Gottlieb
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    Recent Performance Using MILC and QUDA on Various GPU Systems

• ouraman Hajizadeh (Tehran university)
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    Random Matrix Theory for a nano-GPT

• Kieran Holland
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    Simulations of machine-learned RG-improved SU(3) gauge action

• Jessica N. Howard (Kavli Institute for Theoretical Physics)
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    Optimal transport for anomaly detection at LHC

• Gurtej Kanwar (University of Edinburgh)
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    Monte Carlo estimates of flow fields for sampling and noise problems

• Javad Komijani (ETH Zurich)
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    Application of generative models on SU(3) gauge theories in 4 dimension

• Andreas Kronfeld (Fermi National Accelerator Lab. (US))
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    Discussion

• Marina Krstic Marinkovic (ETH Zurich)
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    Closing

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    Welcome

• Thomas Luu (Forshungszentrum Jülich)
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    Machine Learning Low-D Systems

• Tom Magorsch
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    Stochastic differentiation of Monte Carlo simulations for parameter inference in quarkonium suppression

• Julian Mayer-Steudte
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    Optimizing static quark states with Neural Network parametrized general Wilson loops

• Alberto Ramos Martinez (Univ. of Valencia and CSIC (ES))
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    Stochastic automatic differentiation for Lattice QCD

• Fernando Romero López
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    Applications of flow models to the generation of correlated lattice QCD ensembles

• Alexander Rothkopf (Korea University)
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    Protecting continuum symmetries on the lattice - exact prior information for reinforcement learning

• Sinead Ryan (Trinity College Dublin)
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    Discussion

• ankur singha
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    Efficient Multilevel Sampling of Lattice Field Theory Near Criticality

• Octavio Vega
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    Score-Based Diffusion Models for Lattice Gauge Theory

• Evan Weinberg (NVIDIA Corporation)
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    Optimized Multi-Grid Solvers: Power, Precision, and (Matrix) Products

• Urs Rudolf Wenger
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    Machine learning RG-improved SU(3) gauge actions