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Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
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Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In this paper, we present a set of training- and architecture-based methods to construct flow models for targets with multiple separated modes (i.e.~vacua) as well as targets with extended/continuous modes. We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories in their symmetry-broken phases. In this context we investigate different flow-based sampling algorithms, including a composite sampling algorithm where flow-based proposals are occasionally augmented by applying updates using traditional algorithms like HMC.
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Cited by 1 Pith paper
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Leveraging generative models to assist Monte Carlo sampling
This paper is a tutorial review, not a research contribution: it organizes existing methods for using generative models as proposal distributions, transport maps, and annealing bridges in Monte Carlo sampling.
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