no code implementations • 17 Jan 2024 • Cenk Tüysüz, Su Yeon Chang, Maria Demidik, Karl Jansen, Sofia Vallecorsa, Michele Grossi
This work studies the behavior of EQNN models in the presence of noise.
no code implementations • 27 Feb 2023 • Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima
In this work, we first point out that the tunneling problem is also present for normalizing flows but is shifted from the sampling to the training phase of the algorithm.
no code implementations • 22 Nov 2021 • Kim A. Nicoli, Christopher Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati
Crucially, these models allow for the direct estimation of the free energy at a given point in parameter space.
1 code implementation • 7 Aug 2020 • Karl Jansen, Eike Hermann Müller, Robert Scheichl
This paper discusses hierarchical sampling methods to tame this growth in autocorrelations.
High Energy Physics - Lattice Numerical Analysis Numerical Analysis Computational Physics 81-08, 81T25, 65Y20, 60J22 F.2; J.2
no code implementations • 14 Jul 2020 • Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati
In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic.