Flow-based density of states for complex actions
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Abstract (arXiv)
Emerging sampling algorithms based on normalizing flows have the potential to solve ergodicity problems in lattice calculations. Furthermore, it has been noted that flows can be used to compute thermodynamic quantities which are difficult to access with traditional methods. This suggests that they are also applicable to the density-of-states approach to complex action problems. In particular, flow-based sampling may be used to compute the density directly, in contradistinction to the conventional strategy of reconstructing it via measuring and integrating the derivative of its logarithm. By circumventing this procedure, the accumulation of errors from the numerical integration is avoided completely and the overall normalization factor can be determined explicitly. In this proof-of-principle study, we demonstrate our method in the context of two-component scalar field theory where the O(2) symmetry is explicitly broken by an imaginary external field. First, we concentrate on the zero-dimensional case which can be solved exactly. We show that with our method, the Lee-Yang zeroes of the associated partition function can be successfully located. Subsequently, we confirm that the flow-based approach correctly reproduces the density computed with conventional methods in one- and two-dimensional models.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 1 of its 30 references that are in the map. First posted on arXiv 2 Mar 2022.
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1 of 30 references.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1.
4 papers.
- 2022Aspects of scaling and scalability for flow-based sampling of lattice QCDcited by 26
- 2023Sampling the lattice Nambu-Goto string using Continuous Normalizing Flowscited by 12
- 2022Variational Monte Carlo Approach to Partial Differential Equations with Neural Networkscited by 1
- 2024Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flowscited by 1
- 2024Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows1 Jan 2024
- 2023Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows3 Jul 2023
- 2022Aspects of scaling and scalability for flow-based sampling of lattice QCD14 Nov 2022
- 2022Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks4 Jun 2022