Sampling using gauge equivariant flows
arXiv:2008.05456 · doi:10.1103/PhysRevD.103.074504
Abstract
We develop a flow-based sampling algorithm for lattice gauge theories that is gauge-invariant by construction. Our key contribution is constructing a class of flows on an variable (or on a variable by a simple alternative) that respect matrix conjugation symmetry. We apply this technique to sample distributions of single variables and to construct flow-based samplers for and lattice gauge theory in two dimensions.
24 pages, 19 figures
References in corpus (9)
- Gauge Equivariant Convolutional Networks and the Icosahedral CNN
- Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
- A gauge redundancy-free formulation of compact QED with dynamical matter for quantum and classical computations
- Permutation-equivariant neural networks applied to dynamics prediction
- Normalizing Flows on Riemannian Manifolds
- Equivariant Flows: sampling configurations for multi-body systems with symmetric energies
- Equivariant Hamiltonian Flows
- Trivializations for Gradient-Based Optimization on Manifolds
- Lattice Gauge Theory for Condensed Matter Physics: Ferromagnetic Superconductivity as its Example
Cited by in corpus (63)
- Machine Learning in Nuclear Physics
- An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
- Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
- Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
- Exploring QCD matter in extreme conditions with Machine Learning
- FLAG Review 2024
- Lattice gauge equivariant convolutional neural networks
- Machine-learning hidden symmetries
- Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics
- Efficient Modelling of Trivializing Maps for Lattice Theory Using Normalizing Flows: A First Look at Scalability
- Matrix Model simulations using Quantum Computing, Deep Learning, and Lattice Monte Carlo
- Flow-based sampling for fermionic lattice field theories
- Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
- Gauge Invariant and Anyonic Symmetric Transformer and RNN Quantum States for Quantum Lattice Models
- Neural network reconstruction of the dense matter equation of state from neutron star observables
- Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
- Normalizing flows for atomic solids
- Stochastic normalizing flows as non-equilibrium transformations
- Topological Obstructions to Autoencoding
- Flow-based sampling in the lattice Schwinger model at criticality
- Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
- Gauge equivariant neural networks for quantum lattice gauge theories
- Reconstructing spectral functions via automatic differentiation
- Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window
- Learning Trivializing Gradient Flows for Lattice Gauge Theories
- Normalizing Flows and the Real-Time Sign Problem
- Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
- An equation-of-state-meter for CBM using PointNet
- Fourier-Flow model generating Feynman paths
- Generalization capabilities of translationally equivariant neural networks
- Flow-based density of states for complex actions
- Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
- Introduction to Normalizing Flows for Lattice Field Theory
- Deep Learning Hamiltonian Monte Carlo
- Differentiable Physics: A Position Piece
- Confinement in non-Abelian lattice gauge theory via persistent homology
- Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network
- Generative learning for the problem of critical slowing down in lattice Gross Neveu model
- Generative Neural Samplers for the Quantum Heisenberg Chain
- Parallel Tempered Metadynamics: Overcoming potential barriers without surfing or tunneling
- Gauge covariant neural network for quarks and gluons
- Scaling of Stochastic Normalizing Flows in lattice gauge theory
- E(n) Equivariant Normalizing Flows
- Generative models for sampling of lattice field theories
- Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
- Topology changing update algorithms for SU(3) gauge theory
- Equivariance and generalization in neural networks
- Self-learning Monte Carlo with equivariant Transformer
- Vacuum Decay and Euclidean Lattice Monte Carlo
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
- Scaling flow-based approaches for topology sampling in gauge theory
- Training Invertible Linear Layers through Rank-One Perturbations
- Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
- Group equivariant neural posterior estimation
- Preserving gauge invariance in neural networks
- Continuous normalizing flows on manifolds
- Diffusion Models for SU(2) Lattice Gauge Theory in Two Dimensions
- Group-Equivariant Diffusion Models for Lattice Field Theory
- Equivariant Manifold Flows
- Efficient identification of critical regions via Flow Matching-based Monte Carlo initialization
- A comparative lattice analysis of dark gluebal
- Implicit Riemannian Concave Potential Maps
- Applications of flow models to the generation of correlated lattice QCD ensembles