Machine learning action parameters in lattice quantum chromodynamics

@article{Shanahan2018MachineLA,
  title={Machine learning action parameters in lattice quantum chromodynamics},
  author={Phiala E. Shanahan and Daniel Trewartha and William Detmold},
  journal={arXiv: High Energy Physics - Lattice},
  year={2018},
  pages={094506}
}
Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods promise improved efficiency of lattice calculations, and access to regions of parameter space that are currently computationally intractable, via multi-scale action-matching approaches that necessitate parametric regression of generated lattice datasets. The… 

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