SciPost Submission Page
Super-Resolving Normalising Flows for Lattice Field Theories
by Marc Bauer, Renzo Kapust, Jan Martin Pawlowski, Finn Leon Temmen
Submission summary
| Authors (as registered SciPost users): | Renzo Kapust |
| Submission information | |
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| Preprint Link: | scipost_202502_00013v1 (pdf) |
| Date submitted: | 2025-02-08 10:06 |
| Submitted by: | Kapust, Renzo |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
Abstract
We propose a renormalisation group inspired normalising flow that combines benefits from traditional Markov chain Monte Carlo methods and standard normalising flows to sample lattice field theories. Specifically, we use samples from a coarse lattice field theory and learn a stochastic map to the targeted fine theory. The devised architecture allows for systematic improvements and efficient sampling on lattices as large as $128 \times 128$ in all phases when only having sampling access on a $4\times 4$ lattice. This paves the way for reaping the benefits of traditional MCMC methods on coarse lattices while using normalising flows to learn transformations towards finer grids, aligning nicely with the intuition of super-resolution tasks. Moreover, by optimising the base distribution, this approach allows for further structural improvements besides increasing the expressivity of the model.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block