Physics-Informed Neural Networks construct lattice Dirac operators satisfying the Ginsparg-Wilson relation, reproducing overlap fermions to high accuracy and discovering a Fujikawa-type generalized relation via algebraic search.
Perfect lattice action for asymptotically free theories
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abstract
There exist lattice actions which give cut--off independent physical predictions even on coarse grained lattices. Rotation symmetry is restored, the spectrum becomes exact and, in addition, the classical equations have scale invariant instanton solutions. This perfect action can be made short ranged. It can be determined by combining analytical calculations with numerical simulations on small lattices. We illustrate the method and the benefits on the $d=2$ non--linear $\sigma$--model.
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hep-lat 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results toward the continuum limit using gradient-flow and potential observables.
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Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions
Physics-Informed Neural Networks construct lattice Dirac operators satisfying the Ginsparg-Wilson relation, reproducing overlap fermions to high accuracy and discovering a Fujikawa-type generalized relation via algebraic search.
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Machine learning for four-dimensional SU(3) lattice gauge theories
Machine learning generative models and renormalization-group neural networks are used to enhance gauge field sampling and learn fixed-point actions in 4D SU(3) lattice gauge theories, with presented scaling results toward the continuum limit using gradient-flow and potential observables.