Event-Chain Monte-Carlo Simulations of Dense Soft Matter Systems

Monte Carlo Simulations of Dense Galactic Nuclei

Sonification of Markov Chain Monte Carlo Simulations

Markov chain Monte Carlo (McMC) simulation is a popular computational tool for making inferences from complex, high-dimensional probability densities. Given a particular target density p, the idea behind this technique is to simulate a Markov chain that has p as its stationary distribution. To be successful, the chain needs to be run long enough so that the distribution of the current draw is c...

On Event-Chain Monte Carlo Methods

In this dissertation we consider a pair of continuous-time, non-reversible, rejectionfree, and piecewise deterministic MCMC methods, referred to as Event-Chain Monte Carlo methods; respectively reflect-ECMC and flip-ECMC. We compare the two methods in a handful of settings, and find that in all cases the performance of the reflect-ECMC algorithm is superior. We consider extensions of the algori...

Optimized ensemble Monte Carlo simulations of dense Lennard-Jones fluids.

We apply the recently developed adaptive ensemble optimization technique to simulate dense Lennard-Jones fluids and a particle-solvent model by broad-histogram Monte Carlo techniques. Equilibration of the simulated fluid is improved by sampling an optimized histogram in radial coordinates that shifts statistical weight towards the entropic barriers between the shells of the liquid. Interstitial...

Monte Carlo simulation of dense polymer melts using event chain algorithms.

We propose an efficient Monte Carlo algorithm for the off-lattice simulation of dense hard sphere polymer melts using cluster moves, called event chains, which allow for a rejection-free treatment of the excluded volume. Event chains also allow for an efficient preparation of initial configurations in polymer melts. We parallelize the event chain Monte Carlo algorithm to further increase simula...