Matthias Wolff, Francesco Alesiani, Christof Duhme, Xiaoyi Jiang
The Clifford Kolmogorov-Arnold Network (ClKAN) is a new architecture for approximating functions in Clifford algebra spaces, utilizing Randomized Quasi Monte Carlo methods and novel batch normalization strategies for improved scalability and efficiency.
Researchers have developed a new type of neural network architecture called the Clifford Kolmogorov-Arnold Network (ClKAN), which is designed to efficiently approximate complex mathematical functions. This network is particularly useful in spaces defined by Clifford algebra, which are higher-dimensional mathematical structures. To tackle the challenges of working in these complex spaces, the researchers use a technique called Randomized Quasi Monte Carlo to generate grids, along with new methods for normalizing batches of data. This approach has potential applications in scientific research and engineering, and has been tested on both synthetic and physics-based problems.
