5 papers
An Efficient and Robust Projection Enhanced Interpolation Based Tensor Train Decomposition
Daniel Hayes, Jing-Mei Qiu, Tianyi Shi
The tensor-train (TT) format is a data-sparse tensor representation commonly used in high dimensional data approximations. In order to represent data with interpretability in data…
A Semi-Lagrangian Adaptive Rank (SLAR) Method for High-Dimensional Vlasov Dynamics
Nanyi Zheng, William A. Sands, Daniel Hayes +2
We extend our previous work on a semi-Lagrangian adaptive rank (SLAR) integrator, in the finite difference framework for nonlinear Vlasov-Poisson systems, to the general high-order…
An Adaptive-rank Approach with Greedy Sampling for Multi-scale BGK Equations
William A. Sands, Jing-Mei Qiu, Daniel Hayes +1
In this paper, we propose a novel adaptive-rank method for simulating multi-scale BGK equations, based on a greedy sampling strategy. The method adaptively selects important rows a…
Distributed memory parallel adaptive tensor-train cross approximation
Tianyi Shi, Daniel Hayes, Jing-Mei Qiu
The tensor-train (TT) format is a data-sparse tensor representation commonly used in high dimensional function approximations arising from computational and data sciences. Various…
A Semi-Lagrangian Adaptive-Rank (SLAR) Method for Linear Advection and Nonlinear Vlasov-Poisson System
Nanyi Zheng, Daniel Hayes, Andrew Christlieb +1
High-order semi-Lagrangian methods for kinetic equations have been under rapid development in the past few decades. In this work, we propose a semi-Lagrangian adaptive rank (SLAR)…