Flow-based sampling for multimodal and extended-mode distributions in lattice field theory

• URL: http://arxiv.org/abs/2107.00734v2
• Date: Sat, 15 Feb 2025 00:34:46 GMT
• Title: Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
• Authors: Daniel C. Hackett, Chung-Chun Hsieh, Sahil Pontula, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan,
• Abstract summary: We present a set of training- and architecture-based methods to construct flow models for targets with multiple separated modes.<n>We demonstrate the application of these methods to modeling two-dimensional real and complex scalar field theories.
• Score: 3.9492325196180715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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