A Monte Carlo algorithm to measure probabilities of rare events in cluster-cluster aggregation
arXiv:2210.17267 · doi:10.1016/j.cpc.2023.108727
Abstract
We develop a biased Monte Carlo algorithm to measure probabilities of rare events in cluster-cluster aggregation for arbitrary collision kernels. Given a trajectory with a fixed number of collisions, the algorithm modifies both the waiting times between collisions, as well as the sequence of collisions, using local moves. We show that the algorithm is ergodic by giving a protocol that transforms an arbitrary trajectory to a standard trajectory using valid Monte Carlo moves. The algorithm can sample rare events with probabilities of the order of and lower. The algorithm's effectiveness in sampling low-probability events is established by showing that the numerical results for the large deviation function of constant-kernel aggregation reproduce the exact results. It is shown that the algorithm can obtain the large deviation functions for other kernels, including gelling ones, as well as the instanton trajectories for atypical times. The dependence of the autocorrelation times, both temporal and configurational, on the different parameters of the algorithm is also characterized.
26 pages, 10 figures
References in corpus (8)
- The large deviation approach to statistical mechanics
- Computation of extreme heat waves in climate models using a large deviation algorithm
- Dynamics of Freely Cooling Granular Gases
- Rare event algorithm study of extreme warm summers and heat waves over Europe
- Variational approach to rare event simulation using least-squares regression
- Direct simulation Monte Carlo for new regimes in aggregation-fragmentation kinetics
- Lattice models for ballistic aggregation in one-dimension
- Non-extensive super-cluster states in aggregation with fragmentation