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.
Solutions of the Ginsparg-Wilson relation and improved domain wall fermions
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
We discuss a number of lattice fermion actions solving the Ginsparg-Wilson relation. We also consider short ranged approximate solutions. In particular, we are interested in reducing the lattice artifacts, while avoiding (or suppressing) additive mass renormalization. In this context, we also arrive at a formulation of improved domain wall fermions.
citation-role summary
background 1
citation-polarity summary
fields
hep-lat 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Domain wall fermions recover exact chiral symmetry in the infinite fifth dimension limit and produce an effective four-dimensional operator satisfying the Ginsparg-Wilson relation.
citing papers explorer
-
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.
-
Domain wall fermions
Domain wall fermions recover exact chiral symmetry in the infinite fifth dimension limit and produce an effective four-dimensional operator satisfying the Ginsparg-Wilson relation.