Clifford Algebra

Clifford algebra, a generalization of complex numbers and quaternions, is being actively explored for its ability to represent and manipulate geometric and physical data in a computationally efficient and symmetry-preserving manner. Current research focuses on developing Clifford-algebra-based neural network architectures, particularly equivariant graph neural networks (EGNNs) and related models, for applications in diverse fields like physics, chemistry, and knowledge graph embedding. These advancements offer improved expressive power and efficiency compared to traditional methods, leading to enhanced performance in tasks such as N-body simulations and protein structure analysis, and enabling the development of more accurate and efficient machine learning models for complex systems.