TY - JOUR

T1 - Analysis and compression of six-dimensional gyrokinetic datasets using higher order singular value decomposition

AU - Hatch, D. R.

AU - Del-Castillo-Negrete, D.

AU - Terry, P. W.

PY - 2012/6/1

Y1 - 2012/6/1

N2 - Higher order singular value decomposition (HOSVD) is explored as a tool for analyzing and compressing gyrokinetic data. An efficient numerical implementation of an HOSVD algorithm is described. HOSVD is used to analyze the full six-dimensional (three spatial, two velocity space, and time dimensions) gyrocenter distribution function from gyrokinetic simulations of ion temperature gradient, electron temperature gradient, and trapped electron mode driven turbulence. The HOSVD eigenvalues for the velocity space coordinates decay very rapidly, indicating that only a few structures in velocity space can capture the most important dynamics. In almost all of the cases studied, HOSVD extracts parallel velocity space structures which are very similar to orthogonal polynomials. HOSVD is also used to compress gyrokinetic datasets, an application in which it is shown to significantly outperform the more commonly used singular value decomposition. It is shown that the effectiveness of the HOSVD compression improves as the dimensionality of the dataset increases.

AB - Higher order singular value decomposition (HOSVD) is explored as a tool for analyzing and compressing gyrokinetic data. An efficient numerical implementation of an HOSVD algorithm is described. HOSVD is used to analyze the full six-dimensional (three spatial, two velocity space, and time dimensions) gyrocenter distribution function from gyrokinetic simulations of ion temperature gradient, electron temperature gradient, and trapped electron mode driven turbulence. The HOSVD eigenvalues for the velocity space coordinates decay very rapidly, indicating that only a few structures in velocity space can capture the most important dynamics. In almost all of the cases studied, HOSVD extracts parallel velocity space structures which are very similar to orthogonal polynomials. HOSVD is also used to compress gyrokinetic datasets, an application in which it is shown to significantly outperform the more commonly used singular value decomposition. It is shown that the effectiveness of the HOSVD compression improves as the dimensionality of the dataset increases.

KW - Data compression

KW - Plasma gyrokinetic simulations

KW - Singular value decomposition

UR - http://www.scopus.com/inward/record.url?scp=84859218902&partnerID=8YFLogxK

U2 - 10.1016/j.jcp.2012.02.007

DO - 10.1016/j.jcp.2012.02.007

M3 - Article

AN - SCOPUS:84859218902

SN - 0021-9991

VL - 231

SP - 4234

EP - 4256

JO - Journal of Computational Physics

JF - Journal of Computational Physics

IS - 11

ER -