112 citations · 129 across the 6 of their papers we have counts for
4 papers · 1 filter
Smooth Normalizing Flows
Jonas Köhler, Andreas Krämer, Frank Noé
Normalizing flows are a promising tool for modeling probability distributions in physical systems. While state-of-the-art flows accurately approximate distributions and energies, a…
Progress in deep Markov State Modeling: Coarse graining and experimental data restraints
Andreas Mardt, Frank Noé
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems such as proteins. Especially the inclusion…
Identifying optimal cycles in quantum thermal machines with reinforcement-learning
Paolo Andrea Erdman, Frank Noé
The optimal control of open quantum systems is a challenging task but has a key role in improving existing quantum information processing technologies. We introduce a general frame…
Temperature Steerable Flows and Boltzmann Generators
Manuel Dibak, Leon Klein, Andreas Krämer +1
Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples in thermodynamic eq…