Dynamic Monte Carlo method

About: Dynamic Monte Carlo method is a research topic. Over the lifetime, 13294 publications have been published within this topic receiving 371256 citations.

White Monte Carlo for time-resolved photon migration

Abstract: A novel scheme for fully scalable White Monte Carlo (WMC) has been developed and is used as a forward solver in the evaluation of experimental time-resolved spectroscopy. Previously reported scaling problems are avoided by storing detection events individually, turning spatial and temporal binning into post-simulation activities. The approach is suitable for modeling of both interstitial and noninvasive settings (i.e., infinite and semi-infinite geometries). Motivated by an interest in in vivo optical properties of human prostate tissue, we utilize WMC to explore the low albedo regime of time-domain photon migration--a regime where the diffusion approximation of radiative transport theory breaks down, leading to the risk of overestimating both reduced scattering (mu(s)') and absorption (mu(a)). Experimental work supports our findings and establishes the advantages of Monte Carlo-based evaluation.

Monte carlo and perturbation theory studies of the equation of state of the two-dimensional Lennard-Jones fluid

Abstract: Monte Carlo values for the equation of state of the two-dimensional Lennard-Jones fluid are reported. The agreement with previous simulations is good, with the exception of the critical point region. However, the estimates of the critical temperature and density given here are lower than those previously reported. The first and second-order Barker-Henderson and first-order Weeks-Chandler-Andersen perturbation theories are applied to this system and are found to be satisfactory only at high densities. A semiempirical correction term to the second-order Barker-Henderson theory is constructed. The resulting parameterization of the Monte Carlo data is found to be accurate and useful at all densities and temperatures for which the Monte Carlo data exist.

Adjoint-Weighted Tallies for k-Eigenvalue Calculations with Continuous-Energy Monte Carlo

Abstract: A Monte Carlo method is developed that performs adjoint-weighted tallies in continuous-energy k-eigenvalue calculations. Each contribution to a tally score is weighted by an estimate of the relativ...

Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion

Abstract: Classic techniques for simulating molecular motion, such as the Monte Carlo and molecular dynamics methods, generate individual motion pathways one at a time and spend most of their time trying to escape from the local minima of the energy landscape of a molecule. Their high computational cost prevents them from being used to analyze many pathways. We introduce Stochustic Roadmap Sirrrcllation (SRS), a new approach for exploring the kinetics of molecular motion by simultaneously examining multiple pathways encoded compactly in a graph, called a roadmap. A roadmap is computed by sampling a molecule's conformation space at random. The computation does not suffer from the localminima problem encountered with existing methods. Each path in the roadmap represents a potential motion pathway and is associated with a probability indicating the likelihood that the molecule follows this pathway. By viewing the roadmap as a Markov chain, we can efficiently compute kinetic properties of molecular motion over the entire molecular energy landscape. We also prove that, in the limit, SRS converges to the same distribution as Monte Carlo simulation. To test the effectiveness of our approach, we apply it to the computation of the transmission coefficients for protein folding, an important order parameter that measures the "kinetic distance" of a protein's conformation to its native state Our computational studies show that SRS obtains more accurate results and achieves several orders- of- magnitude reduction in computation time, compared with Monte Carlo simulatio.