一种基于布朗粒子的混合搜索模型

基于网络上的布朗粒子运动基本原理,提出了一种单粒子和多粒子相结合的混合搜索模型.该模型将一次搜索过程分成单粒子搜索与多粒子搜索两个阶段,既克服了单粒子搜索效率低下的缺点,又降低了多粒子搜索的硬件代价.在各种复杂网络拓扑上实施该模型,并与混合导航模型进行比较.结果表明,混合搜索模型的平均搜索时间收敛更快,硬件代价更小.将度大优先的目标选择策略与混合搜索模型相结合,能进一步提高搜索效率.此外通过仿真发现,在无标度网络上混合搜索模型的效率远高于单粒子随机行走,与多粒子随机行走的效率相当,但硬件代价远小于多粒子行走.最后针对该模型给出了一种能有效降低负载的"吸收"策略.     

运用CTRW-Metropolis模型数值研究亚稳势中粒子逃逸问题

基于连续时间随机行走(CTRW)理论,实现反常扩散条件下对跳跃步长和等待时间分布函数的抽样,改进Metropolis抽样判定方法以适用于存在非线性势的情况.数值研究布朗粒子在亚稳势下的逃逸速率.结果显示,稳定逃逸速率γ_st随反常指数α非单调变化,在超扩散条件下存在极大值和位垒相消现象.     

蒙特卡罗计算中颗粒型燃料的随机分布模型比较

高温气冷堆是新一代反应堆系统的热门候选堆型,已经受到国际上越来越多的关注。为设计和分析这种堆型,因其特有的包覆颗粒燃料引入了双重非均匀性,需要应用随机分布模型。对粗网格模型、细网格随机(FLS)模型、随机顺序添加(RSA)模型、子网格随机(Sub-FLS)模型和Metropolis模型等进行了研究,通过计算分析比较给出了各种模型的优缺点。结果表明:子网格随机模型和连续的RSA模型非常接近参考值,但是连续RSA模型的建模时间随着燃料体积份额的增加连续快速上升。 Key words: coated particle fuels; stochastic transport model; Monte Carlo; random distribution     

蒙特卡罗计算中颗粒型燃料的随机分布模型比较

高温气冷堆是新一代反应堆系统的热门候选堆型,已经受到国际上越来越多的关注。为设计和分析这种堆型,因其特有的包覆颗粒燃料引入了双重非均匀性,需要应用随机分布模型。对粗网格模型、细网格随机(FLS)模型、随机顺序添加(RSA)模型、子网格随机(Sub-FLS)模型和Metropolis模型等进行了研究,通过计算分析比较给出了各种模型的优缺点。结果表明：子网格随机模型和连续的RSA模型非常接近参考值,但是连续RSA模型的建模时间随着燃料体积份额的增加连续快速上升。 Key words: coated particle fuels; stochastic transport model; Monte Carlo; random distribution     

非一致随机分布复合材料力学参数预测的统计二阶双尺度方法

通过建立统计的二阶双尺度计算方法,预测了非一致随机分布复合材料结构的力学参数,包括刚度参数和弹性极限强度参数.所谓非一致随机分布复合材料结构,是指在整个结构中夹杂随机分布,但分布特征并不是处处相同,而是逐渐变化的,从而导致材料在宏观上具有随着位置连续变化的力学性能.描述了一致和非一致随机分布复合材料结构的特征及其细观表征方法,并建立了统计的二阶双尺度计算公式,讨论了材料的弹性极限强度准则.最后,针对不同的非一致随机分布复合材料,预测了材料的力学参数并与实验数据进行了对比.结果表明,统计的二阶双尺度方法对于预测非一致随机分布复合材料的力学参数是有效的. 关键词： 统计二阶双尺度方法 非一致随机分布 复合材料结构     

一类权重网络的加速演化模型

采用动态形成权重网络的方法，研究了在演化过程中新增边具有加速连接情况下权重网络的拓扑特性和强度分布，给出了节点强度与度的解析表达式.分析表明，加速演化的权重模型具有明显的无标度特性.再者，只要权重网络的边权重服从某一概率分布，则在演化过程中强度择优连接与度择优连接对于网络的度分布没有影响，且与具体的概率分布无关.     

利用单分子光学探针测量幂律分布的聚合物动力学

研究聚合物薄膜纳米尺度的动力学特性对于高性能材料的制备具有重要的意义.本文利用尼罗红单分子作为光学探针吸附在聚丙烯酸甲酯(PMA)聚合物链上,研究该聚合物薄膜的动力学特性.通过单分子散焦宽场荧光成像显微镜技术测量了单分子随PMA聚合物链转动弛豫的三维再取向特性,当环境温度高于PMA的玻璃点温度19 K时,发现处于PMA聚合物薄膜中的单分子光学探针的转动态和非转动态的持续时间概率密度服从指数截止的幂律分布.研究结果表明该温度下PMA聚合物薄膜的纳米环境动力学仍存在空间和时间异构性.     

基于随机抽样法的多群核数据不确定性影响分析

基于随机抽样方法,研究多群核数据不确定性对反应堆物理计算的影响。首先利用SCALE软件包中核数据协方差矩阵和自主开发的随机抽样模块SAMP,得到多群微观截面等核数据的抽样值,之后分别使用SCALE/TRITON和PARCS程序进行组件计算及堆芯稳态计算,最后通过统计分析得到组件和堆芯计算结果的不确定度。以Almaraz压水堆核电厂装载的燃料组件和首循环堆芯为对象,研究了不同燃耗下有效增殖因子、动力学参数、核素浓度和双群均匀化宏观截面等组件计算结果,以及堆芯功率分布等堆芯计算结果的不确定度。分析结果表明：组件计算结果不确定度多随燃耗变化,快群宏观截面不确定度总体高于热群;堆芯计算结果受核数据不确定性影响显著,其中稳态径向功率分布的最大不确定度为1.9%左右。     

外场作用下BaF分子发射光子累积量及等待时间分布的研究

利用产生函数方法对BaF分子在激光场与射频场作用下发射光子的累积量及等待时间分布进行了研究. 累积量κ₁ 和κ₂ 表明在固有偶极和射频场的作用下, 系统表现出外场辅助吸收的现象, 即当Δ₁=nω_rf时, 系统存在吸收峰. 二阶、三阶等待时间分布呈现出明显的振荡现象.     

一种基于随机行走和策略连接的网络演化模型

本文提出了一个基于随机行走和策略选择的复杂网络局域演化模型RAPA. 新节点加入系统不需要全局知识,而是通过随机行走构造局域世界;然后依据概率采用随机连接,"扶贫"连接或"亲富"连接策略,从局域世界中选择节点增加连接边;最终自组织演化具有幂律特点的复杂网络. 初步的解析计算和仿真实验都表明,RAPA模型不仅重现了具有小世界特性、整体上的无标度特性,还可以演化出小变量饱和以及指数截断等现象,同时也具有明显的聚类特性,并能够构造出同配或异配等不同混合模式的网络. 关键词： 复杂网络 模型 随机行走 策略连接     

A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization

Stochastic gradient sg-based algorithms for Markov chain Monte Carlo sampling (sgmcmc) tackle large-scale Bayesian modeling problems by operating on mini-batches and injecting noise on sgsteps. The sampling properties of these algorithms are determined by user choices, such as the covariance of the injected noise and the learning rate, and by problem-specific factors, such as assumptions on the loss landscape and the covariance of sg noise. However, current sgmcmc algorithms applied to popular complex models such as Deep Nets cannot simultaneously satisfy the assumptions on loss landscapes and on the behavior of the covariance of the sg noise, while operating with the practical requirement of non-vanishing learning rates. In this work we propose a novel practical method, which makes the sg noise isotropic, using a fixed learning rate that we determine analytically. Extensive experimental validations indicate that our proposal is competitive with the state of the art on sgmcmc.     

Photon transport in thin disordered slabs

We examine using Monte Carlo simulations, photon transport in optically ‘thin’ slabs whose thickness L is only a few times the transport mean free path l*, with particles of different scattering anisotropies. The confined geometry causes an auto-selection of only photons with looping paths to remain within the slab. The results of the Monte Carlo simulations are borne out by our analytical treatment that incorporates directional persistence by the use of the Ornstein-Uhlenbeck process, which interpolates between the short time ballistic and long time diffusive regimes.     

Stochastic One-Dimensional Lorentz Gas on a Lattice

We study a one-dimensional stochastic Lorentz gas where a light particle moves in a fixed array of nonidentical random scatterers arranged in a lattice. Each scatterer is characterized by a random transmission/reflection coefficient. We consider the case when the transmission coefficients of the scatterers are independent identically distributed random variables. A symbolic program is presented which generates the exact velocity autocorrelation function (VACF) in terms of the moments of the transmission coefficients. The VACF is found for different types of disorder for times up to 20 collision times. We then consider a specific type of disorder: a two-state Lorentz gas in which two types of scatterers are arranged randomly in a lattice. Then a lattice point is occupied by a scatterer whose transmission coefficient is with probability p or + with probability 1–p. A perturbation expansion with respect to is derived. The ² term in this expansion shows that the VACF oscillates with time, the period of oscillation being twice the time of flight from one scatterer to its nearest neighbor. The coarse-grained VACF decays for long times like t ^–3/2, which is similar to the decay of the VACF of the random Lorentz gas with a single type of scatterer. The perturbation results and the exact ones (found up to 20 collision times) show good agreement.     

A Spatial Stochastic Model for Virus Dynamics

We introduce a spatial stochastic model for virus dynamics. We show that if the death rate of infected cells increases too fast with the virus load the virus dies out. This is in sharp contrast with what happens in the (non-spatial deterministic) basic model for virus dynamics. AMS 1991 Subject Classification: 60K35     

随机反应扩散格气模型上钙动力学的粗粒化模拟

发展了一种基于随机格气模型的粗粒化方法,该方法能有效模拟内质网表面钙动力学信息. 首先将相邻的微观节点合并成粗粒化节点,再根据局域平均场近似推导出粗粒化反应速率,然后执行粗粒化动力学蒙特卡洛模拟. 发现粗粒化动力学蒙特卡洛模拟结果和微观模拟结果非常吻合. 有趣的是,存在一个最佳的粗粒化比m,使得粗粒化模拟与微观模拟的相变点偏差最小. 固定m,发现临界点随体系尺度增加而单调增加,而且相变点的偏差与体系尺度存在一个标度关系.此外,该粗粒化方法大大地加快了蒙特卡洛模拟速率,并且与微观模拟直接相关. 该方法可以广泛用来研究体系尺度效应,而节省大量计算时间.     

Critical exponents for the self-avoiding random walk in three dimensions

We compute by direct Monte Carlo simulation the main critical exponents, , ₄, andv and the effective coordination number for the self-avoiding random walk in three dimensions on a cubic lattice. We find both hyperscaling relationsdv=2– anddv– 2 ₄+=0 satisfied ind = 3.     

Random walk on distant mesh points Monte Carlo methods

A new technique for obtaining Monte Carlo algorithms based on the Markov chains with a finite number of states is suggested. Instead of the classical random walk on neighboring mesh points, a general way of constructing Monte Carlo algorithms that could be called random walk on distant mesh points is considered. It is applied to solve boundary value problems. The numerical examples indicate that the new methods are less laborious and therefore more efficient.In conclusion, we mention that all Monte Carlo algorithms are parallel and could be easily realized on parallel computers.     

High-precision Monte Carlo test of the conformai-invariance predictions for two-dimensional mutually avoiding walks

Let _l be the critical exponent associated with the probability thatl independentN-step ordinary random walks, starting at nearby points, are mutually avoiding. Using Monte Carlo methods combined with a maximum-likelihood data analysis, we find that in two dimensions ₂=0.6240±0.0005±0.0011 and ₃=1.4575±0.0030±0.0052, where the first error bar represents systematic error due to corrections to scaling (subjective 95% confidence limits) and the second error bar represents statistical error (classical 95% confidence limits). These results are in good agreement with the conformal-invariance predictions ₂=5/8 and ₃=35/24.     

一种改进的计算探测器校正因子的相关抽样方法

对于小尺寸探测器处于大块介质的情形, 在探测器的校正因子的Monte Carlo模拟中, 存在两个难题: 一是由于探测器尺寸很小粒子难以到达探测器并发生碰撞; 二是两个随机变量比值难以达到要求精度. 本文使用经过改进的粒子碰撞自动重要抽样方法, 再结合相关抽样方法, 解决了这两个难题, 并在MCNP-4C程序平台上加以实现. 除了粒子碰撞自动重要抽样以外, 还选用了其他3种方法: 直接模拟、区域分裂、强迫碰撞+Dxtran球分别与相关抽样方法结合, 对一个简化的探测器校正因子计算模型进行了计算. 实际计算结果表明, 相关抽样方法无论与哪种方法结合, 都起到了提高相关量计算效率的作用; 而它与粒子碰撞自动重要抽样结合, 比其他方法具有明显的优越性.     

Error Estimation in the Histogram Monte Carlo Method

We examine the sources of error in the histogram reweighting method for Monte Carlo data analysis. We demonstrate that, in addition to the standard statistical error which has been studied elsewhere, there are two other sources of error, one arising through correlations in the reweighted samples, and one arising from the finite range of energies sampled by a simulation of finite length. We demonstrate that while the former correction is usually negligible by comparison with statistical fluctuations, the latter may not be, and give criteria for judging the range of validity of histogram extrapolations based on the size of this latter correction.