Nonnegative Tensor-Train Low-Rank Approximations of the Smoluchowski Coagulation Equation

被引：2
|
作者：
Manzini, Gianmarco [1 ]
Skau, Erik [2 ]
Truong, Duc P. [2 ]
Vangara, Raviteja [1 ]
机构：
[1] Los Alamos Natl Lab, Div Theoret, Los Alamos, NM 87545 USA
[2] Los Alamos Natl Lab, Comp Computat & Stat Div, Los Alamos, NM 87545 USA
来源：
LARGE-SCALE SCIENTIFIC COMPUTING (LSSC 2021) | 2022年 / 13127卷
关键词：
Smoluchowski equation; Multidimensional problem; Nonnegative tensor factorization; Low-order tensor decomposition; Tensor-train method; MONTE-CARLO; ALGORITHMS; SIMULATION; MATRIX;
D O I：
10.1007/978-3-030-97549-4_39
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
We present a finite difference approximation of the nonnegative solutions of the two dimensional Smoluchowski equation by a nonnegative low-order tensor factorization. Two different implementations are compared. The first one is based on a full tensor representation of the numerical solution and the coagulation kernel. The second one is based on a tensor-train decomposition of solution and kernel. The convergence of the numerical solution to the analytical one is investigated for the Smoluchowski problem with the constant kernel and the influence of the nonnegative decomposition on the solution accuracy is investigated.
引用
收藏
页码：342 / 350
页数：9
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