An energy-biased method to evaluate ensemble averages requiring test-particle insertion is presented. The method is based on biasing the sampling within the subdomains of the test-particle configurational space with energies smaller than a given value freely assigned. These energy wells are located via unbiased random insertion over the whole configurational space and are sampled using the so-c...

A theoretical analysis of the Monte Carlo method for steady-state semiconductor device simulation, also known as the single-particle Monte Carlo method, is presented. At the outset of the formal treatment is the stationary Boltzmann equation supplemented by boundary conditions, which is transformed into an integral equation. The conjugate equation has been formulated in order to develop forward...

Solution: Let G(V,E) denote the graph in question; we construct a new graph H(W,F ) in which an ordinary single particle random walk corresponds to the two particle random walk on G. Take W = V × V = {(v1, v2)|v1, v2 ∈ V } and F = {((v1, v2), (w1, w2))|(v1, w1), (v2, w2) ∈ E}. The idea here is that a uniform random walk on H encodes the state of a two particle random walk on G (in the same way ...

An energy-biased method to evaluate ensemble averages requiring test-particle insertion is presented. The method is based on biasing the sampling within the subdomains of the test-particle configurational space with energies smaller than a given value freely assigned. These energy-wells are located via unbiased random insertion over the whole configurational space and are sampled using the so c...

Solution: Let G(V,E) denote the graph in question; we construct a new graph H(W,F ) in which an ordinary single particle random walk corresponds to the two particle random walk on G. Take W = V × V = {(v1, v2)|v1, v2 ∈ V } and F = {((v1, v2), (w1, w2))|(v1, w1), (v2, w2) ∈ E}. The idea here is that a uniform random walk on H encodes the state of a two particle random walk on G (in the same way ...

We present a new impulse-based method, called the Tethered Particle System (TPS), for the dynamic simulation of deformable biological structures. The TPS is unusual in that it may capture a gradual process of deformation using only instantaneous impulses that occur in response to particle collisions. This paper describes the method and its application to synaptic vesicle clusters and deformable...

Many integrable stochastic particle systems in one space dimension (such as TASEP - totally asymmetric simple exclusion process and its various deformations, with a notable exception of ASEP) remain when we equip each $x_i$ own jump rate parameter $\nu_i$. It is consequence integrability that the distribution $x_n(t)$ system started from step initial configuration depends on parameters $\nu_j$,...

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. Being a stochastic algorithm, PSO and its randomness present formidable challenge for the theoretical analysis of it, and few of the existing PSO improvements have make an effort to eliminate the random coefficients in the PSO updating formula. This paper analyzes the importance of the randomness in the...

We present a numerical method that consistently implements thermal fluctuations and hydrodynamic interactions to the motion of Brownian particles dispersed in incompressible host fluids. In this method, the thermal fluctuations are introduced as random forces acting on the Brownian particles. The hydrodynamic interactions are introduced by directly resolving the fluid motions with the particle ...