E(n)-equivariant graph neural network
E(n)-equivariant graph neural networks are a class of neural network architectures designed to process graph-structured data while preserving equivariance under the Euclidean group E(n), which includes rotations, translations, and reflections in n-dimensional space. These models are particularly important in applications involving geometric data, such as molecular modeling and physical simulations.