A generalized guided Monte Carlo algorithm

A new technique for metropolis Monte Carlo simulation to capture aggregate structures of fine particles: Cluster-moving Monte Carlo algorithm

In typical particle simulations applied to device problems, it is desirable to simulate regions having widely different carrier concentration with similar resolution of the energy distribution function. Standard Monte Carlo (MC) algorithms use variance reduction techniques to limit the statistical error in regions of low carrier density. Here, we describe an alternative approach, based on a local iterative MC algorithm, where the carrier distribution is calculated by an iterative application of short MC steps for test particles that represent the local density. The information obtained from the local MC procedure is used to compute directly the evolution of the energy distribution function from an initial distribution until a steady state is reached. This local iterative procedure treats the charge density, represented by the MC, with arbitrary weight, independently of the actual number of physical particles corresponding to a given density. In this way, the computation time is the same for low- or high-density regions, leading to a more efficient use of computational resources and uniform statistical error. The computation time can be reduced also by tabulating and using more than once the results from the local MC steps in the overall iteration process. While the local iterative MC approach does not have the generality of the standard MC technique, much faster calculations and greatly reduced statistical noise are possible when applied to steady-state problems. We present a number of simulation examples for silicon devices, which illustrate the capabilities of the approach.

We numerically analyze the feasibility of a platform-neutral, general strategy to perform quantum simulations of fermionic lattice field theories under open boundary conditions. The digital quantum simulator requires solely one- and two-qubit gates and is scalable since integrating each Hamiltonian term requires a finite (non-scaling) cost. The exact local fermion encoding we adopt relies on auxiliary Z2 lattice gauge fields by adding a pure gauge Hamiltonian term akin to the Toric Code. By numerically emulating the quantum simulator real-time dynamics, we observe a timescale separation for spin- and charge-excitations in a spin-12 Hubbard ladder in the t−J model limit.

The three-dimensional Heisenberg spin glass model with ±J interactions is studied using an over-relaxed Monte Carlo algorithm. We have measured the correlation length ξ, and using the crossing of ξ∕L for different L find a finite temperature spin glass transition, with TSG=0.220(5). In addition, we have varied the number of Monte Carlo steps used prior to, and during, thermal averaging to control the effects of finite time averaging. We find that the over-relaxation algorithm allows for an accurate measurements using as few as 300 Monte Carlo steps.

Optimal arrangement of turbines in wind farms is very important to achieve maximum energy at the lowest cost. In the present study, the use of Vestas V-47 wind turbine and uniform one-way wind in achieving the optimal arrangement of horizontal axis turbines in Manjil with genetic and Monte Carlo algorithms has been investigated. Jensen model is used to simulate the wake effect on the downstream turbines. The objective function is considered as the ratio of cost to power of the power plant. The results show that the Monte Carlo method compared with genetic algorithm will give a better result. Under the same conditions, the Monte Carlo algorithm will give 29% and 40% better results in terms of the number of turbines and output power, respectively. In terms of optimization, in the Monte Carlo algorithm, its fitness value is 16% less than the genetic algorithm, which indicates its better optimization.

Choice of integrator in the hybrid Monte Carlo algorithm

Here r/ is a random number (usually Gaussian) with (q(x)* ~/(y))=2ax,y and e is the step size. Without the noise term the algorithm would be simply a technique for finding the minima of S[~b] by the gradient method. The noise term simulates quantum fluctuations about these classical configurations. In common with other techniques, the Langevin simulation according to (2) will suffer from critical slowing down for large lattices and long correlation lengths 4. This is because the components of ~b with highest momenta evolve 4 2 times faster than those with the lowest momenta and therefore the amount of computing time required to study the infrared structure of configurations on the lattice grows as (volume). 4 2 . The remedy to this problem is a technique known as Fourier acceleration. One simply chooses a larger step size e for low p relative to

Identification of resonance parameters from the finite volume energy spectrum

Stochastic automatic differentiation for Monte Carlo processes

The equation of motion and molecular dynamics for simulating lattice field theory using the Hybrid Monte Carlo (HMC) algorithm are described. Some techniques for improving the HMC efficiency are discussed.

We apply the hybrid Monte Carlo (HMC) algorithm to the financial time sires analysis of the stochastic volatility (SV) model for the first time. The HMC algorithm is used for the Markov chain Monte Carlo (MCMC) update of volatility variables of the SV model in the Bayesian inference. We compute parameters of the SV model from the artificial financial data and compare the results from the HMC algorithm with those from the Metropolis algorithm. We find that the HMC decorrelates the volatility variables faster than the Metropolis algorithm. We also make an empirical analysis based on the Yen/Dollar exchange rates.

Monte Carlo source detection of atmospheric emissions and error functions analysis

Monte Carlo analysis of non compact QED lattice theory with Higgs fields

Alpha matter on a lattice

A new micromagnetic method using the hybrid Monte Carlo (HMC) algorithm has been developed, and the magnetic properties at finite temperature can be simulated. In this paper, the HMC micromagnetics are applied on the polycrystalline FePt–C granular films for heat-assisted magnetic recording, where the significant parameters such as the temperature-dependent saturation magnetization $M_{s}$ (T) are measured by experiments. The Voronoi polycrystalline microstructure is built upon a regular mesh of micromagnetic cells. The magnetization (M–H) loops below the Curie temperature are calculated; they agree well with experiments at 300, 400, 500, and 600 K, respectively, which have confirmed the validity of the HMC micromagnetic method. Besides, the M–H loop at 700 K, which currently cannot be measured by experiments, can also be simulated using the HMC algorithm, which shows superparamagnetic properties.