Speaker
Description
Numerical simulations of quantum field theories are indispensable for precision studies of the Standard Model, yet extracting continuum physics from discretized spacetime remains challenging due to lattice artifacts. Renormalization-group improved fixed-point (FP) actions provide an elegant solution by suppressing these artifacts, but their complexity has long prevented their practical use. Advances in machine learning, in particular lattice gauge-equivariant convolutional neural networks (L-CNNs) [1], enable accurate parameterizations of FP actions.
In this talk, we present recent results from our collaboration [2], demonstrating that Monte Carlo simulations using machine-learned FP actions for four-dimensional SU(3) gauge theory drastically reduce discretization effects in gradient-flow observables, allowing continuum physics to be extracted reliably even at coarse lattice spacings. Furthermore, we establish that the gradient flow of this action is tree-level artifact-free to all orders in the lattice spacing, proving its classically perfect nature and validating the quality of the L-CNN parameterization without introducing secondary artifacts.
Finally, we report on ongoing work exploring LLM-guided evolutionary optimization of custom GPU kernels for gauge-equivariant neural networks using the OpenEvolve framework. We discuss the challenges and insights of targeting memory consumption and execution efficiency, demonstrating how automated code evolution can help scale physics-informed AI architectures to large-scale scientific applications.
[1] M. Favoni, A. Ipp, D. I. Müller, D. Schuh, Phys. Rev. Lett. 128 (2022), 032003, https://doi.org/10.1103/PhysRevLett.128.032003 , https://arxiv.org/abs/2012.12901
[2] K. Holland, A. Ipp, D. I. Müller, U. Wenger, Phys. Rev. Lett. 136, 031901 (2026), https://doi.org/10.1103/k41k-2pnc , https://arxiv.org/abs/2504.15870