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From the 1 of 7 linked papers with an AI index.

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7 papers

hep-lat2026

Lattice Configuration Generation with a Self-Learning Diffusion Model

Akio Tomiya

The paper presents a diffusion‑based sampler that self‑trains without any pre‑generated training data to generate lattice‑field configurations, and validates its accuracy on the tw…

hep-lat2026

Numerical Hints for Dyon Condensation at via Wilson-'t Hooft Loops in Yang-Mills Theory

Hiromasa Watanabe, Issaku Kanamori, Okuto Morikawa +3

Yang-Mills theories at and are unitarily equivalent, but their periodicity has a nontrivial realization. Recent developments in generalized global symmetries sh…

hep-ph2026

Thermodynamics in symmetry-improved Cornwall-Jackiw-Tomboulis formalism: application to the low-energy effective theory of QCD

Yuepeng Guan, Mamiya Kawaguchi, Shinya Matsuzaki +1

We study the thermodynamics of the symmetry-improved Cornwall-Jackiw-Tomboulis (SICJT) formalism and apply it to a low-energy effective theory of QCD. In the symmetry-improved form…

hep-lat2026

Parameter Optimization of Domain-Wall Fermion using Machine Learning

Shunsuke Yasunaga, Kenta Yoshimura, Akio Tomiya +1

We study a parameter optimization of domain-wall fermions to improve chiral symmetry based on machine learning. Domain-wall fermions involve coefficients along the fifth dimension,…

hep-lat2026

Lattice Gauge Theory via LLVM-Level Automatic Differentiation

Yuki Nagai, Akio Tomiya, Hiroshi Ohno

We enable the automatic construction of Hybrid Monte Carlo (HMC) forces in lattice gauge theory by performing reverse-mode automatic differentiation at the level of optimized LLVM…

hep-lat2025

JuliaQCD: Portable lattice QCD package in Julia language

Yuki Nagai, Akio Tomiya

We develop a new lattice gauge theory code set JuliaQCD using the Julia language. Julia is well-suited for integrating machine learning techniques and enables rapid prototyping and…