Temperature steerable flows and Boltzmann generators
Manuel Dibak, Leon Klein, Andreas Krämer, Frank Noé
Abstract
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 equilibrium. The equilibrium distribution is usually defined by an energy function and a thermodynamic state. Here, we propose temperature steerable flows (TSFs) which are able to generate a family of probability densities parametrized by a choosable temperature parameter. TSFs can be embedded in generalized ensemble sampling frameworks to sample a physical system across multiple thermodynamic states.
Topics & Concepts
Statistical physicsBoltzmann distributionBoltzmann constantMonte Carlo methodSampling (signal processing)Maxwell–Boltzmann distributionPhysicsComputer scienceMathematicsStatisticsThermodynamicsPlasmaQuantum mechanicsDetectorOpticsLattice Boltzmann Simulation StudiesTheoretical and Computational PhysicsModel Reduction and Neural Networks