9 papers
A Mass, Momentum, and Energy Conserving Semi-Lagrangian Adaptive-Rank (SLAR) Method for the Vlasov-Poisson System
Nanyi Zheng, William A. Sands, Jing-Mei Qiu
We propose a semi-Lagrangian adaptive-rank (SLAR) method that combines the large time-step capability of semi-Lagrangian schemes with the efficiency of adaptive-rank tensor represe…
A conservative adaptive rank method for the Wigner-Poisson system
Andrew Christlieb, Sining Gong, F. Alejandro Padilla-Gomez +1
We propose a conservative adaptive rank method for the 1D1V Wigner-Poisson system. The method targets a central challenge in deterministic quantum kinetic simulations: reducing the…
Efficient Sketching-Based Summation of Tucker Tensors
Rudi Smith, Mirjeta Pasha, Andrés Galindo-Olarte +5
We present efficient, sketching-based methods for the summation of tensors in Tucker format. Leveraging the algebraic structure of Khatri-Rao and Kronecker products, our approach e…
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…