Statistics – Computation
Scientific paper
Mar 2012
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012aps..apr.s1052d&link_type=abstract
American Physical Society, APS April Meeting 2012, March 31-Apr 3, 2012, abstract #S1.052
Statistics
Computation
Scientific paper
Tensor product decomposition (TPD) methods are a powerful linear algebra technique for the efficient representation of high dimensional data sets. In the simplest 2-dimensional case, TPD reduces to the singular value decomposition (SVD) of matrices. These methods, which are closely related to proper orthogonal decomposition techniques, have been extensively applied in signal and image processing, and to some fluid mechanics problems. However, their use in plasma physics computation is relatively new. Some recent applications include: data compression of 6-dimensional gyrokinetic plasma turbulence data sets, ootnotetextD. R. Hatch, D. del-Castillo-Negrete, and P. W. Terry. Submitted to Journal Comp. Phys. (2011). noise reduction in particle methods, ootnotetextR. Nguyen, D. del-Castillo-Negrete, K. Schneider, M. Farge, and G. Chen: Journal of Comp. Phys. 229, 2821-2839 (2010). and multiscale analysis of plasma turbulence. ootnotetextS. Futatani, S. Benkadda, and D. del-Castillo-Negrete: Phys. of Plasmas, 16, 042506 (2009) The goal of this presentation is to discuss a novel application of TPD methods to projective integration of particle-based collisional plasma transport computations.
No associations
LandOfFree
Tensor product decomposition methods for plasmas physics computations does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Tensor product decomposition methods for plasmas physics computations, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Tensor product decomposition methods for plasmas physics computations will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1368419