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Machine-Learned Renormalization-Group-Improved Gauge Actions and Classically Perfect Gradient Flows

Kieran Holland1,*, Andreas Ipp2,†, David I. Müller2,‡, and Urs Wenger3,§

  • 1University of the Pacific, 3601 Pacific Avenue, Stockton, California 95211, USA
  • 2Institute for Theoretical Physics, TU Wien, Wiedner Hauptstraße 8-10/136, A-1040 Vienna, Austria
  • 3Albert Einstein Center for Fundamental Physics, Institute for Theoretical Physics, University of Bern, Sidlerstraße 5, CH-3012 Bern, Switzerland

  • *Contact author: kholland@pacific.edu
  • †Contact author: ipp@hep.itp.tuwien.ac.at
  • ‡Contact author: dmueller@hep.itp.tuwien.ac.at
  • §Contact author: wenger@itp.unibe.ch

Future Requirements of Lattice Field Theory Calculations on European High-Performance Computing Facilities

Gert Aarts, Gunnar Bali, Jacob Finkenrath, Stefan Krieg, Antonio Rago, Carsten Urbach, and Hartmut Wittig

Procedia Computer Science 286 24 (2026)

Lattice fermion formulation via physics-informed neural networks: Ginsparg-Wilson relation and overlap fermions

Tatsuhiro Misumi

Phys. Rev. D 114 014511 (2026)

Approximating Grassmann valued path integrals with radial basis function neural networks

Gábor Balassa

Phys. Rev. D 114 036024 (2026)

Scale setting of SU(N) Yang–Mills theory, topology and large- N volume independence

Claudio Bonanno, Jorge Luis Dasilva Golán, Margarita García Pérez, Massimo D’Elia, and Andrea Giorgieri

Phys. Rev. D 113 074501 (2026)

Wilson loops with neural networks

Verena Bellscheidt, Nora Brambilla, Andreas S. Kronfeld, and Julian Mayer-Steudte

Phys. Rev. D 113 094501 (2026)

Testing machine-learned distributions against Monte Carlo data for the QCD chiral phase transition

Reinhold Kaiser, Frithjof Karsch, Jan Philipp Klinger, Owe Philipsen, Christian Schmidt, and Simran Singh

J. High Energ. Phys. 2026 271 (2026)

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