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.
Boydaet al., Applications of Machine Learning to Lat- tice Quantum Field Theory, inSnowmass 2021(2022) arXiv:2202.05838 [hep-lat]
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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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CNN-Based Online Trigger for QGP Event Selection
CNN trigger for QGP events reaches 83.7% accuracy on reconstructed Au+Au events at 30 AGeV after training on PHSD and cross-validation on UrQMD, with deployment via lightweight C++ package.
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