4 papers
Exploring Group Convolutional Networks for Sign Problem Mitigation via Contour Deformation
Christoph Gäntgen, Thomas Luu, Marcel Rodekamp
The sign problem that arises in Hybrid Monte Carlo calculations can be mitigated by deforming the integration manifold. While simple transformations are highly efficient for simula…
Search for Stable States in Two-Body Excitations of the Hubbard Model on the Honeycomb Lattice
Petar Sinilkov, Evan Berkowitz, Thomas Luu +1
We present one- and two-body measurements for the Hubbard model on the honeycomb (graphene) lattice from ab-initio quantum monte carlo simulations. Of particular interest is excito…
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…
Mitigating the Hubbard Sign Problem. A Novel Application of Machine Learning
Marcel Rodekamp, Christoph Gäntgen
Many fascinating systems suffer from a severe (complex action) sign problem preventing us from calculating them with Markov Chain Monte Carlo simulations. One promising method to a…