Flow-based generative models for Markov chain Monte Carlo in lattice field theory
By Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan. 2019
281 citations recorded by the source corpus.
A Markov chain update scheme using a machine-learned flow-based generative model is proposed for Monte Carlo sampling in lattice field theories. The generative model may be optimized (trained) to produce samples from a distribution approximating the desired Boltzmann distribution determined by the lattice action of the theory being studied. Training the model systematically improves autocorrelation times in the Markov chain, even in regions of parameter space where standard Markov chain Monte Carlo algorithms exhibit critical slowing down in producing decorrelated updates. Moreover, the model may be trained without existing samples from the desired distribution. The algorithm is compared with HMC and local Metropolis sampling for ϕ4 theory in two dimensions.
Subfields: Condensed Matter Physics, Mathematical Physics, Statistics and Probability, Probabilistic & Bayesian Methods
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In Condensed Matter Physics
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In Probabilistic & Bayesian Methods
- Frank No'e
- Leon Klein
- Jonas Köhler
- Hao Wu
- Alvaro Sanchez-Gonzalez
- Peter W. Battaglia
- Meire Fortunato
- Steven L. Brunton
Citations in the corpus
- Stochastic Normalizing Flows (2020, methodology): “…flows with importance sampling methods to Boltzmann Generators (No ´ e et al., 2019), an approach now popular in molecular and material sciences and nuclear physics (M¨uller et al., 2018; Li & Wang, 2018; No ´ e et al., 2019; K ¨ ohler et al., 2019; Albergo et al., 2019; Nicoli et al., 2019).”
- Equivariant Flows: exact likelihood generative learning for symmetric densities (2020, background): “Copyright 2020 by the author(s). approach is widely applicable in the physical sciences, and has also been employed in the sampling of spin lattice states (Nicoli et al., 2019; Li & Wang, 2018) and nuclear physics models (Albergo et al., 2019).”
- Learning Mesh-Based Simulation with Graph Networks (2020, background): “High resolution simulations are often very slow, and learned models can provide faster predictions, reducing turnaround time for workflows in engineering and science [14, 6, 44, 18, 1].”