1 citations · 1 across the 4 of their papers we have counts for
4 papers
Tackling the Sign Problem in the Doped Hubbard Model with Normalizing Flows
Dominic Schuh, Lena Funcke, Janik Kreit +2
The Hubbard model at finite chemical potential is a cornerstone for understanding doped correlated systems, but simulations are severely limited by the sign problem. In the auxilia…
Toward Scalable Normalizing Flows for the Hubbard Model
Janik Kreit, Andrea Bulgarelli, Lena Funcke +4
Normalizing flows have recently demonstrated the ability to learn the Boltzmann distribution of the Hubbard model, opening new avenues for generative modeling in condensed matter p…
Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows
Dominic Schuh, Janik Kreit, Evan Berkowitz +4
We present the first proof of principle that normalizing flows can accurately learn the Boltzmann distribution of the fermionic Hubbard model - a key framework for describing the e…
Simulating the Hubbard Model with Equivariant Normalizing Flows
Dominic Schuh, Janik Kreit, Evan Berkowitz +4
Generative models, particularly normalizing flows, have shown exceptional performance in learning probability distributions across various domains of physics, including statistical…