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相关论文: A Monte Carlo simulation for kinetic chemotaxis mo…

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Mathematical models have been widely used to describe the collective movement of bacteria by chemotaxis. In particular, bacterial concentration waves traveling in a narrow channel have been experimentally observed and can be precisely…

偏微分方程分析 · 数学 2016-04-15 Casimir Emako , Charlène Gayrard , Axel Buguin , Luís Neves de Almeida , Nicolas Vauchelet

This article reviews the basic computational techniques for carrying out multi-scale simulations using statistical methods, with the focus on simulations of epitaxial growth. First, the statistical-physics background behind Monte Carlo…

材料科学 · 物理学 2009-04-17 Peter Kratzer

We present a kinetic Monte Carlo method for simulating chemical transformations specified by reaction rules, which can be viewed as generators of chemical reactions, or equivalently, definitions of reaction classes. A rule identifies the…

定量方法 · 定量生物学 2010-07-09 Jin Yang , Michael I. Monine , James R. Faeder , William S. Hlavacek

Dynamic Monte Carlo simulations are used to study coupled transport (co-transport) through sub-nanometer-diameter pores. In this classic Hodgkin-Keynes mechanism, an ion species uses the large flux of an abundant ion species to move against…

软凝聚态物质 · 物理学 2013-11-27 Dezső Boda , Éva Csányi , Dirk Gillespie , Tamás Kristóf

We present a new algorithm based on a Cartesian mesh for the numerical approximation of kinetic models for chemosensitive movements set in an arbitrary geometry. We investigate the influence of the geometry on the collective behavior of…

数值分析 · 数学 2013-03-12 Francis Filbet , Chang Yang

Kinetic approaches are routinely employed to simulate the dynamics of systems that are too rarified to be described by the Navier-Stokes equations. However, generally they are far too computationally expensive to be applied for systems that…

流体动力学 · 物理学 2014-04-18 Irina Sagert , Dirk Colbry , Terrance Strother , Rodney Pickett , Wolfgang Bauer

A Monte Carlo method based on a density-of-states sampling is proposed for study of arbitrary statistical mechanical ensembles in a continuum. A random walk in the two-dimensional space of particle number and energy is used to estimate the…

软凝聚态物质 · 物理学 2009-11-07 Qiliang Yan , Roland Faller , Juan J. de Pablo

To better understand the capture process by a nanopore, we introduce an efficient Kinetic Monte Carlo (KMC) algorithm that can simulate long times and large system sizes by mapping the dynamic of a point-like particle in a 3D spherically…

生物物理 · 物理学 2021-03-22 Le Qiao , Maxime Ignacio , Gary W. Slater

We have developed and implemented a numerical evolution scheme for a class of stochastic problems in which the temporal evolution occurs on widely-separated time scales, and for which the slow evolution can be described in terms of a small…

计算物理 · 物理学 2009-09-29 S. Setayeshgar , C. W. Gear , H. G. Othmer , I. G. Kevrekidis

Biological tissues are complex structures composed of many elements which make light-based tissue diagnostics challenging. Over the past decades, Monte Carlo technique has been used as a fundamental and versatile approach toward modeling…

光学 · 物理学 2024-05-17 Maryam Ghahremani

Chemotaxis is the physical phenomenon that bacteria adjust their motions according to chemical stimulus. A classical model for this phenomenon is a kinetic equation that describes the velocity jump process whose tumbling/transition kernel…

偏微分方程分析 · 数学 2024-01-11 Kathrin Hellmuth , Christian Klingenberg , Qin Li , Min Tang

Monte Carlo simulation is one of the most important tools in the study of diffusion processes. For constant diffusion coefficients, an appropriate Gaussian distribution of particle's steplengths can generate exact results, when compared…

计算物理 · 物理学 2015-06-12 V. Ruiz Barlett , M. Hoyuelos , H. O. Mártin

The kinetic Monte Carlo method is a standard approach for simulating physical systems whose dynamics are stochastic or that evolve in a probabilistic manner. Here we show how to calculate the system time for such simulations.

计算物理 · 物理学 2008-01-14 Clinton DeW. Van Siclen

The kinetic Monte Carlo method is used to model the dynamic properties of proton diffusion in anhydrous proton conductors. The results have been discussed with reference to a two-step process called the Grotthuss mechanism. There is a…

We describe collective-move Monte Carlo algorithms designed to approximate the overdamped dynamics of self-assembling nanoscale components equipped with strong, short-ranged and anisotropic interactions. Conventional Monte Carlo simulations…

统计力学 · 物理学 2012-04-16 Stephen Whitelam

We investigate numerically a model consisting in a kinetic equation for the biased motion of bacteria following a run-and-tumble process, coupled with two reaction-diffusion equations for chemical signals. This model exhibits asymptotic…

偏微分方程分析 · 数学 2018-11-26 Vincent Calvez , Laurent Gosse , Monika Twarogowska

Monte Carlo statistical ray-tracing methods are commonly employed to simulate carrier transport in nanostructured materials. In the case of a large degree of nanostructuring and under linear response (small driving fields), these…

介观与纳米尺度物理 · 物理学 2023-02-09 Pankaj Priyadarshi , Neophytos Neophytou

We introduce mesoscopic and macroscopic model equations of chemotaxis with anomalous subdiffusion for modelling chemically directed transport of biological organisms in changing chemical environments with diffusion hindered by traps or…

动力系统 · 数学 2012-12-03 T. A. M. Langlands , B. I. Henry

We report on the development of a test-particle based kinetic Monte Carlo code for large systems and its application to simulate matter in the continuum regime. Our code combines advantages of the Direct Simulation Monte Carlo and the…

流体动力学 · 物理学 2014-07-31 Irina Sagert , Wolfgang Bauer , Dirk Colbry , Rodney Pickett , Terrance Strother

We introduce a Monte Carlo algorithm to efficiently compute transport properties of chaotic dynamical systems. Our method exploits the importance sampling technique that favors trajectories in the tail of the distribution of displacements,…

统计力学 · 物理学 2018-05-25 Diego Tapias , David P. Sanders , Eduardo G. Altmann
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