116 citations · 294 across the 24 of their papers we have counts for
5 papers · 1 filter
Incremental Hierarchical Tucker Decomposition
Doruk Aksoy, Alex A. Gorodetsky
We present two new algorithms for approximating and updating the hierarchical Tucker decomposition of tensor streams. The first algorithm, Batch Hierarchical Tucker - leaf to root…
An Incremental Tensor Train Decomposition Algorithm
Doruk Aksoy, David J. Gorsich, Shravan Veerapaneni +1
We present a new algorithm for incrementally updating the tensor train decomposition of a stream of tensor data. This new algorithm, called the {\em tensor train incremental core e…
Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis
John D. Jakeman, Michael Eldred, Gianluca Geraci +1
In this paper, we present an adaptive algorithm to construct response surface approximations of high-fidelity models using a hierarchy of lower fidelity models. Our algorithm is ba…
Randomized Functional Sparse Tucker Tensor for Compression and Fast Visualization of Scientific Data
Prashant Rai, Hemanth Kolla, Lewis Cannada +1
We propose a strategy to compress and store large volumes of scientific data represented on unstructured grids. Approaches utilizing tensor decompositions for data compression have…
A continuous analogue of the tensor-train decomposition
Alex A. Gorodetsky, Sertac Karaman, Youssef M. Marzouk
We develop new approximation algorithms and data structures for representing and computing with multivariate functions using the functional tensor-train (FT), a continuous extensio…