Gauge Equivariant Neural Network

Gauge-equivariant neural networks (G-NNs) are designed to incorporate physical symmetries, specifically gauge invariance, directly into their architecture, improving accuracy and efficiency in modeling systems governed by these symmetries. Current research focuses on developing and applying various G-NN architectures, including lattice-equivariant convolutional networks (L-CNNs) and gauge-equivariant Volterra networks (GEVNets), to diverse problems such as lattice quantum chromodynamics (QCD) simulations, fluid dynamics, and medical image analysis. This approach offers significant advantages in terms of computational cost, generalization ability, and the ability to capture essential physical properties, leading to improved performance in various scientific and engineering applications.