Noise-cancellation algorithm for simulations of Brownian particles
arXiv:2401.12577 · doi:10.1103/PhysRevE.109.015303
Abstract
We investigate the usage of a recently introduced noise-cancellation algorithm for Brownian simulations to enhance the precision of measuring transport properties such as the mean-square displacement or the velocity-autocorrelation function. The algorithm is based on explicitly storing the pseudo-random numbers used to create the randomized displacements in computer simulations and subtracting them from the simulated trajectories. The resulting correlation function of the reduced motion is connected to the target correlation function up to a cross-correlation term. Using analytical theory and computer simulations, we demonstrate that the cross-correlation term can be neglected in all three systems studied in this aper. We further expand the algorithm to Monte Carlo simulations and analyze the performance of the algorithm and rationalize that it works particularly well for unbounded, weakly interacting systems in which the precision of the mean-square displacement can be improved by orders of magnitude.
arXiv comment10 pages, 9 figures
References in corpus (9)
- How far from equilibrium is active matter?
- Enhanced sampling methods for molecular dynamics simulations
- Brownian motion: a paradigm of soft matter and biological physics
- Active Brownian and inertial particles in disordered environments: short-time expansion of the mean-square displacement
- Persistent memory for a Brownian walker in a random array of obstacles
- Dynamic density functional theories for inhomogeneous polymer systems compared to Brownian dynamics simulations
- Directed transport of Brownian particles in a double symmetric potential
- Persistent anti-correlations in Brownian dynamics simulations of dense colloidal suspensions revealed by noise suppression
- Mobility, response and transport in non-equilibrium coarse-grained models