Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
This work addresses sampling challenges in lattice field theory, a domain-specific problem in physics, with incremental improvements to existing flow-based methods.
The paper tackled the problem of sampling multimodal and extended-mode distributions in lattice field theory by developing training and architecture methods for flow-based generative models, achieving successful application to two-dimensional scalar field theories in symmetry-broken phases.
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.