# 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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