Hierarchical Approximate Proper Orthogonal Decomposition
Hierarchical Approximate Proper Orthogonal Decomposition is a scholarly work, published in 2018 in ''SIAM Journal on Scientific and Statistical Computing''. The main subjects of the publication include uncertainty quantification, finite element method, and physics-informed neural networks. This\nwork presents a generic, easy-to-implement approach to compute an approximate\nPOD based on arbitrary tree hierarchies of worker nodes, where each worker\ncomputes a POD of only a small amount of input vectors.