222 citations · 961 across the 42 of their papers we have counts for
6 papers · 1 filter
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…
Building Normalizing Flows with Stochastic Interpolants
Michael S. Albergo, Eric Vanden-Eijnden
A generative model based on a continuous-time normalizing flow between any pair of base and target probability densities is proposed. The velocity field of this flow is inferred fr…
Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +11
Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gaug…
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda +9
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic deter…
Non-Hertz-Millis scaling of the antiferromagnetic quantum critical metal via scalable Hybrid Monte Carlo
Peter Lunts, Michael S. Albergo, Michael Lindsey
A key component of the phase diagram of many iron-based superconductors and electron-doped cuprates is believed to be a quantum critical point (QCP), delineating the onset of antif…
Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer +7
Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodyna…