Temperature-steerable flows
arXiv:2012.00429
Abstract
Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples of a physical system's equilibrium density. The equilibrium distribution is usually defined by an energy function and a thermodynamic state, such as a given temperature. Here we propose temperature-steerable flows (TSF) which are able to generate a family of probability densities parametrized by a choosable temperature parameter. TSFs can be embedded in a generalized ensemble sampling framework such as parallel tempering in order to sample a physical system across thermodynamic states, such as multiple temperatures.
References in corpus (9)
- NICE: Non-linear Independent Components Estimation
- Normalizing Flows: An Introduction and Review of Current Methods
- Density estimation using Real NVP
- Flow-based generative models for Markov chain Monte Carlo in lattice field theory
- Neural Network Renormalization Group
- Asymptotically unbiased estimation of physical observables with neural samplers
- Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
- Equivariant Hamiltonian Flows
- Monge-Ampère Flow for Generative Modeling