Continuous normalizing flows for lattice gauge theories
- URL: http://arxiv.org/abs/2410.13161v1
- Date: Thu, 17 Oct 2024 02:30:44 GMT
- Title: Continuous normalizing flows for lattice gauge theories
- Authors: Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng,
- Abstract summary: We present a general continuous normalizing flow architecture for matrix Lie groups that is equivariant under group transformations.
We apply this to lattice gauge theories in two dimensions as a proof-of-principle and demonstrate competitive performance.
- Score: 8.901227918730562
- License:
- Abstract: Continuous normalizing flows are known to be highly expressive and flexible, which allows for easier incorporation of large symmetries and makes them a powerful tool for sampling in lattice field theories. Building on previous work, we present a general continuous normalizing flow architecture for matrix Lie groups that is equivariant under group transformations. We apply this to lattice gauge theories in two dimensions as a proof-of-principle and demonstrate competitive performance, showing its potential as a tool for future lattice sampling tasks.
Related papers
- Practical applications of machine-learned flows on gauge fields [36.54062796409407]
Normalizing flows are machine-learned maps between different lattice theories.
We discuss and demonstrate two applications of flows in replica exchange (parallel tempering) sampling.
arXiv Detail & Related papers (2024-04-17T18:17:14Z) - Theory of mobility edge and non-ergodic extended phase in coupled random
matrices [18.60614534900842]
The mobility edge, as a central concept in disordered models for localization-delocalization transitions, has rarely been discussed in the context of random matrix theory.
We show that their overlapped spectra and un-overlapped spectra exhibit totally different scaling behaviors, which can be used to construct tunable mobility edges.
Our model provides a general framework to realize the mobility edges and non-ergodic phases in a controllable way in RMT.
arXiv Detail & Related papers (2023-11-15T01:43:37Z) - Normalizing flows for lattice gauge theory in arbitrary space-time
dimension [135.04925500053622]
Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions.
We discuss masked autoregressive with tractable and unbiased Jacobian determinants, a key ingredient for scalable and exact flow-based sampling algorithms.
For concreteness, results from a proof-of-principle application to SU(3) gauge theory in four space-time dimensions are reported.
arXiv Detail & Related papers (2023-05-03T19:54:04Z) - Aspects of scaling and scalability for flow-based sampling of lattice
QCD [137.23107300589385]
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing.
It remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations.
arXiv Detail & Related papers (2022-11-14T17:07:37Z) - Gauge-equivariant flow models for sampling in lattice field theories
with pseudofermions [51.52945471576731]
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as estimators for the fermionic determinant.
This is the default approach in state-of-the-art lattice field theory calculations, making this development critical to the practical application of flow models to theories such as QCD.
arXiv Detail & Related papers (2022-07-18T21:13:34Z) - Toward Learning Robust and Invariant Representations with Alignment
Regularization and Data Augmentation [76.85274970052762]
This paper is motivated by a proliferation of options of alignment regularizations.
We evaluate the performances of several popular design choices along the dimensions of robustness and invariance.
We also formally analyze the behavior of alignment regularization to complement our empirical study under assumptions we consider realistic.
arXiv Detail & Related papers (2022-06-04T04:29:19Z) - Stochastic normalizing flows as non-equilibrium transformations [62.997667081978825]
We show that normalizing flows provide a route to sample lattice field theories more efficiently than conventional MonteCarlo simulations.
We lay out a strategy to optimize the efficiency of this extended class of generative models and present examples of applications.
arXiv Detail & Related papers (2022-01-21T19:00:18Z) - Equivariant Discrete Normalizing Flows [10.867162810786361]
We focus on building equivariant normalizing flows using discrete layers.
We introduce two new equivariant flows: $G$-coupling Flows and $G$-Residual Flows.
Our construction of $G$-Residual Flows are also universal, in the sense that we prove an $G$-equivariant diffeomorphism can be exactly mapped by a $G$-residual flow.
arXiv Detail & Related papers (2021-10-16T20:16:00Z) - Boundary theories of critical matchgate tensor networks [59.433172590351234]
Key aspects of the AdS/CFT correspondence can be captured in terms of tensor network models on hyperbolic lattices.
For tensors fulfilling the matchgate constraint, these have previously been shown to produce disordered boundary states.
We show that these Hamiltonians exhibit multi-scale quasiperiodic symmetries captured by an analytical toy model.
arXiv Detail & Related papers (2021-10-06T18:00:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.