5 citations · 5 across the 2 of their papers we have counts for
5 papers
Progress in Normalizing Flows for 4d Gauge Theories
Ryan Abbott, Denis Boyda, Daniel C. Hackett +4
Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensio…
Topological data analysis of the deconfinement transition in SU(3) lattice gauge theory
Daniel Spitz, Julian M. Urban, Jan M. Pawlowski
We study the confining and deconfining phases of pure lattice gauge theory with topological data analysis. This provides unique insights into long range correlatio…
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +10
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological…
Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning
Stefan Bluecher, Lukas Kades, Jan M. Pawlowski +2
Machine learning has the potential to aid our understanding of phase structures in lattice quantum field theories through the statistical analysis of Monte Carlo samples. Available…
Spectral Reconstruction with Deep Neural Networks
Lukas Kades, Jan M. Pawlowski, Alexander Rothkopf +5
We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem. Our a…