Stochastic Particle Approximations for Smoluchoski’s Coagualtion Equation

By A. Eibeck, W. Wagner. 2001

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Convergence of the Marcus–Lushnikov Process (2004, background): “1) We also have an explicit solution to (MS), see [3].”
Reducing Statistical Noise and Extending the Size Spectrum by Applying Weighted Simulation Particles in Monte Carlo Simulation of Coagulation (2009, methodology): “Following these ideas, the mass flow algorithm (for the mass concentration size distribution m(v, t)) (Eibeck and Wagner 2001) and the direct simulation algorithm (for the number concentration size distribution n(v, t)) (Goodson and Kraft 2002), which are event-driven and combine with the acceptance”
From the discrete to the continuous coagulation–fragmentation equations (2002, background): “, [8, 13, 14, 17, 29] for the analysis of stochastic algorithms.”
Simulating the evolution of soot mixing state with a particle-resolved aerosol model (2008, methodology): “This noise could be reduced by averaging several simulations in a Monte Carlo fashion, or by using a variable number of physical particles per computational particle, as in the Mass Flow Algorithm [Babovsky , 1999; Eibeck and Wagner , 2001].”