4 papers
Solving sign problems with physics-informed kernels
Friederike Ihssen, Renzo Kapust, Jan M. Pawlowski
In the present work we construct a novel generative architecture for systems with complex probability distributions. In general, these sampling tasks come with two challenges: reso…
Generative sampling with physics-informed kernels
Friederike Ihssen, Renzo Kapust, Jan M. Pawlowski
We construct a generative network for Monte-Carlo sampling in lattice field theories and beyond, for which the learning of layerwise propagation is done and optimised independently…
Super-Resolving Normalising Flows for Lattice Field Theories
Marc Bauer, Renzo Kapust, Jan M. Pawlowski +1
We propose a renormalisation group inspired normalising flow that combines benefits from traditional Markov chain Monte Carlo methods and standard normalising flows to sample latti…
Harmonically trapped fermions in one dimension: A finite temperature lattice Monte Carlo study
Felipe Attanasio, Marc Bauer, Renzo Kapust +1
We study a one-dimensional two-component Fermi gas in a harmonic trapping potential using finite temperature lattice quantum Monte Carlo methods. We are able to compute observables…