Scalable

We propose a domain-decomposed method for Multivariate Functional Approximations (MFA) using B-spline bases, enhancing scalability and accuracy in large datasets. Our approach minimizes local errors and recovers high-order continuity at subdomain interfaces, optimizing communication costs. Performance results demonstrate its efficiency and scalability across various datasets in 1D, 2D, and 3D.