Compressed sensing-based smoke simulation

Author

Ruixue Zhang ; Xubo Yang

Author_Institution

Sch. of Software, Shanghai Jiao Tong Univ., Shanghai, China

fYear

2012

fDate

16-18 Oct. 2012

Firstpage

241

Lastpage

244

Abstract

As we known, high-dimension smoke simulation is time consuming because of large amount of data sampling and computing. We present a method that introduces compressed sensing to smoke simulation. This method could significantly reduce the sampling data and the simulation time, while giving a convincing effect. We use the wavelet basis as compressing basis, and OMP as the reconstruction method, and reconstruct high-dimension smoke from low-dimension data frame by frame. Our method is fast and easy to implement, and the data sampling could not obey the Nyquist sampling theorem which could reduce the amount of data.

Keywords

compressed sensing; compressible flow; flow simulation; physics computing; sampling methods; smoke; two-phase flow; wavelet transforms; Nyquist sampling theorem; OMP; compressed sensing-based smoke simulation; compressing basis; data computing; data sampling; high-dimension smoke reconstruction; high-dimension smoke simulation; low-dimension data frame; reconstruction method; simulation time; wavelet basis; Compressed sensing; Computational modeling; Data models; Equations; Image reconstruction; Kernel; Mathematical model; compressed sensing; fast; reduce sampling data; smoke simulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing (CISP), 2012 5th International Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4673-0965-3

Type

conf

DOI

10.1109/CISP.2012.6469963

Filename

6469963