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000619664 1001_ $$0P:(DE-HGF)0$$aNicoli, Kim A.$$b0$$eCorresponding author
000619664 1112_ $$a40th International Symposium on Lattice Field Theory$$cBatavia$$d2023-07-30 - 2023-08-05$$gLattice 2023$$wUnited States
000619664 245__ $$aNeuLat: a toolbox for neural samplers in lattice field theories
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000619664 520__ $$aThe application of normalizing flows for sampling in lattice field theory has garnered considerable attention in recent years. Despite the growing community at the intersection of machine learning (ML) and lattice field theory, there is currently a lack of a software package that facilitates efficient software development for new ideas in this field. We present NeuLat, a fully customizable software package that unifies recent advances in the fast-growing field of deep generative models for lattice field theory in a single software library. NeuLat is designed to be modular, supports a variety of lattice field theories as well as normalizing flow architectures, and is easily extensible. We believe that NeuLat has the potential to considerably simplify the application and benchmarking of ML methods for lattice quantum field theories and beyond
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000619664 7001_ $$0P:(DE-HGF)0$$aAnders, Christopher J.$$b1
000619664 7001_ $$0P:(DE-HGF)0$$aFuncke, Lena$$b2
000619664 7001_ $$0P:(DE-H253)PIP1003636$$aJansen, Karl$$b3
000619664 7001_ $$0P:(DE-HGF)0$$aNakajima, Shinichi$$b4
000619664 7001_ $$0P:(DE-HGF)0$$aKessel, Pan$$b5
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000619664 773__ $$0PERI:(DE-600)2642026-0$$a10.22323/1.453.0286$$p286$$tProceedings of Science / International School for Advanced Studies$$v(LATTICE2023)$$x1824-8039$$y2023
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