Welcome

• Marina Krstic Marinkovic (ETH Zurich)

Room: HCI G7

Optimal transport for anomaly detection at LHC

• Jessica N. Howard (Kavli Institute for Theoretical Physics)

Room: HCI G7

Machine learning RG-improved SU(3) gauge actions

• Urs Wenger (University of Bern)

Room: HCI G7 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.

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

• Tom Magorsch

Room: HCI G7 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 expensive to evaluate and the number of input parameters increases. In this talk I will discuss the approach of differentiating the simulator, which enables parameter inference by gradient descent. For Monte Carlo simulations involving discrete probabilities it is possible to build on the REINFORCE gradient estimator to differentiate the full stochastic simulation. I demonstrate this method for the simulation of quarkonium suppression. Quarkonium suppression refers to the phenomenon of bound states dissociating in the quark gluon plasma and can be measured by the nuclear modification factor R_{AA}. The underlying simulator that predicts this suppression solves a Lindblad equation by sampling stochastic trajectories. I showcase how to obtain a low variance gradient estimator and fit the quarkonium transport coefficients to nuclear modification factor data.

Optimizing static quark states with Neural Network parametrized general Wilson loops

• Julian Mayer-Steudte

Room: HCI G7

Protecting continuum symmetries on the lattice - exact prior information for reinforcement learning

• Alexander Rothkopf (Korea University)

Room: HCI G7

Discussion

• Nora brambilla

Room: HCI G7

Monte Carlo estimates of flow fields for sampling and noise problems

• Gurtej Kanwar (University of Edinburgh)

Room: HCI G7

Applications of flow models to the generation of correlated lattice QCD ensembles

• Fernando Romero López

Room: HCI G7 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 limits of gauge theories, the mass dependence of QCD observables, hadronic matrix elements based on the Feynman-Hellmann approach, and the computation of glueball correlators. In all cases, statistical uncertainties are significantly reduced when machine-learned flows are incorporated as compared with the same calculations performed with uncorrelated ensembles or direct reweighting.

Applications of Stochastic Normalizing Flows to gauge theories and defects

• Elia Cellini (University of Turin / INFN Turin)

Room: HCI G7 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 with volume in lattice SU(3) gauge theory. Afterward, I will discuss theories with defects and present a general strategy for applying flow-based samplers to such systems. In particular, I will showcase an application of our approach to scalar field theory for calculating entanglement entropy, as well as an application to SU(3) gauge theory aimed at addressing topological freezing.

Application of generative models on SU(3) gauge theories in 4 dimension

• Javad Komijani (ETH Zurich)

Room: HCI G7

Score-Based Diffusion Models for Lattice Gauge Theory

• Octavio Vega

Room: HCI G7

Discussion

• Marina Krstic Marinkovic (ETH Zurich)
• Sinead Ryan (Trinity College Dublin)

Room: HCI G7

Progress in learning contour deformations for observables

• William Detmold

Room: HCI G7

Diffusion models for lattice field theory

• Gert Aarts (Swansea University)

Room: HCI G7

Machine Learning Low-D Systems

• Thomas Luu (Forshungszentrum Jülich)

Room: HCI G7 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 offer a great testbed for testing novel algorithms that could potentially be used in lattice gauge theory simulations.

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

• Kieran Holland

Room: HCI G7

Efficient Multilevel Sampling of Lattice Field Theory Near Criticality

• Ankur Singha

Room: HCI G7 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 approach enables high Effective Sample Size (ESS) and mitigates critical slowing down. Our results demonstrate improved scalability and accuracy over traditional and superresolution-based methods.

Stochastic automatic differentiation for Lattice QCD

• Alberto Ramos Martinez (Univ. of Valencia and CSIC (ES))

Room: HCI G7

Discussion

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

Room: HCI G7

Random Matrix Theory for a nano-GPT

• Ouraman Hajizadeh (Independent Researcher)

Room: HCI G7

Optimized Multi-Grid Solvers: Power, Precision, and (Matrix) Products

• Evan Weinberg (NVIDIA Corporation)

Room: HCI G7

Recent Performance Using MILC and QUDA on Various GPU Systems

• Steven Gottlieb

Room: HCI G7 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 ensemble with a lattice spacing of about 0.03 fm and physically light quarks.

Closing

• Marina Krstic Marinkovic (ETH Zurich)

Room: HCI G7