This paper introduces a new model to learn graph neural networks equivariant to rotations,
translations, reflections and permutations called E (n)-Equivariant Graph Neural Networks …

This paper introduces a generative model equivariant to Euclidean symmetries: E (n)
Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E (n) …

Predictive modeling of various systems around the world is extremely essential from the
physics and engineering perspectives. The recognition of different systems and the capacity …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …

Motivated by the vast success of deep convolutional networks, there is a great interest in
generalizing convolutions to non-Euclidean manifolds. A major complication in comparison …

Graph neural network (GNN) is a promising approach to learning and predicting physical
phenomena described in boundary value problems, such as partial differential equations …

The combination of neural networks and numerical integration can provide highly accurate
models of continuous-time dynamical systems and probabilistic distributions. However, if a …

Graph neural network (GNN) is a promising approach to learning and predicting physical
phenomena described in boundary value problems, such as partial differential equations …

Geometric deep learning, ie, designing neural networks to handle the ubiquitous geometric
data such as point clouds and graphs, have achieved great successes in the last decade …