A quasi-stochastic simulation of the general dynamics equation for aerosols
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Abstract
We propose a quasi-Monte Carlo (QMC) scheme for the simulation of the general dynamics equation (GDE) for aerosols. The mass distribution is approximated by a fixed number of weighted numerical particles. Time is discretized and a splitting approach is used. Coagulation is simulated with a stochastic particle method using quasi-random points. In addition, the particles are reordered by increasing size at every time step. Integration of condensation/evaporation and deposition is performed using a deterministic particle method. The accuracy of the scheme is assessed through several numerical experiments, in cases where an exact solution is known. The error of the QMC scheme is shown to be smaller than the error produced by the corresponding Monte Carlo (MC) scheme.
© de Gruyter 2007
Articles in the same Issue
- The weighted variance minimization for options pricing
- A quasilinear stochastic partial differential equation driven by fractional white noise
- A quasi-stochastic simulation of the general dynamics equation for aerosols
- Skewed distributions generated by the Student's t kernel
- Expansion of random boundary excitations for elliptic PDEs
- Monte Carlo estimators for small sensitivity indices
- A use of algorithms for numerical modeling of order statistics
Articles in the same Issue
- The weighted variance minimization for options pricing
- A quasilinear stochastic partial differential equation driven by fractional white noise
- A quasi-stochastic simulation of the general dynamics equation for aerosols
- Skewed distributions generated by the Student's t kernel
- Expansion of random boundary excitations for elliptic PDEs
- Monte Carlo estimators for small sensitivity indices
- A use of algorithms for numerical modeling of order statistics
