Non Euclidean

Non-Euclidean geometry extends traditional geometric concepts beyond flat spaces, enabling the modeling of complex, hierarchical, and richly structured data prevalent in modern science and machine learning. Current research focuses on adapting machine learning models, such as neural networks and graph-based methods, to these non-Euclidean spaces, often employing techniques like hyperbolic embeddings, manifold learning, and geometric neural operators. This work is significant because it allows for more accurate and efficient analysis of data with inherent non-Euclidean structure, impacting fields ranging from phylogenetics and image processing to graph analysis and robotics.