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

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

Daniel C. Hackett
(
- MIT, Cambridge, CTP and
- IAIFI, Cambridge
)
,
Chung-Chun Hsieh
(
- Taiwan, Natl. Taiwan U.
)
,
Sahil Pontula
(
- MIT
)
,
Michael S. Albergo
(
- New York U., CCPP
)
,
Denis Boyda
(
- Argonne, ALCF and
- MIT, Cambridge, CTP and
- IAIFI, Cambridge
)

Jul 1, 2021

3 pages

e-Print:

2107.00734 [hep-lat]

Report number:

MIT-CTP/5312,FERMILAB-PUB-25-0090-T,
MIT-CTP/5312

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Abstract: (arXiv)

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.

Note:

38+3 pages, 39 figures. v2: major revisions including new application to extended modes

field theory: scalar
vacuum state: multiple
dimension: 2
lattice field theory
composite
performance
flow

References(97)

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