Machine learning for lattice field theory and beyond

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$λφ^4$ Scalar Neural Network Field Theory

Continuous flows and transfer learning

Data-driven discovery of relevant information in many-body problems: from spin lattice models to quantum field simulators

Deep Learning Inverse Problems in Extreme QCD Matter Study

Deforming complex-valued distributions via machine learning

Discussion

Disentangling representations in Restricted Boltzmann Machines without adversaries

EFT-inspired generative models for simulations of quantum field theories

Gauge-equivariant multigrid neural networks

Global and local symmetries in neural networks

Gradient estimators without action derivative in Schwinger model.

Inferring effective couplings with Restricted Boltzmann Machines

Instantaneous gauge field generation with approximate trivializing maps

Interpretable order parameters from persistent homology in non-Abelian lattice gauge theory

Learning about the Hubbard Model

Machine Learned Thermodynamics of Physical Systems Across Critical Phases

Machine learning a fixed point action

Machine learning a fixed point action

Machine Learning assisted real-time simulations with Complex Langevin

Mitigating signal-to-noise problems using learned contour deformations

Normalizing Flows for Effective String Theory

Path gradient estimators for CNFs in Lattice Gauge Theory

Renormalization Group Approach for Machine Learning Hamiltonian

Scalar field Restricted Boltzmann Machine as an ultraviolet regulator

Statistical mechanics of deep learning beyond the infinite-width limit

Stochastic normalizing flows as out-of-equilibrium transformations

The Restricted Boltzman Machine: Phase Diagram, Generation and Interpretability

Thimbology and Qubits

Training a Gomoku-Agent using DRL

Trivializing map as a coarse-graining map

Using equivariant neural networks as maps of gauge field configurations

Visualizing the inner workings of L-CNNs

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