PINNs optimize Dirac operators to satisfy the Ginsparg-Wilson relation, reproducing overlap fermions and autonomously recovering both the standard and a Fujikawa-type generalized GW relation via polynomial ansatz search.
Perfect lattice action for asymptotically free theories
3 Pith papers cite this work. Polarity classification is still indexing.
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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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VAPOR is a variational quantum algorithm that finds RG fixed points for naively discretized operators in a symmetry-restricted SU(2) Yang-Mills toy model by decomposing into Pauli strings.
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.
citing papers explorer
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Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions
PINNs optimize Dirac operators to satisfy the Ginsparg-Wilson relation, reproducing overlap fermions and autonomously recovering both the standard and a Fujikawa-type generalized GW relation via polynomial ansatz search.
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Implementing Hamiltonian Renormalization Group Flow on Quantum Computers with VAPOR
VAPOR is a variational quantum algorithm that finds RG fixed points for naively discretized operators in a symmetry-restricted SU(2) Yang-Mills toy model by decomposing into Pauli strings.
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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.