非定常输运问题适应于消息传递并行编程环境的香农熵计算方法

在多计算步的非定常输运问题的蒙特卡罗模拟中,为自动调整每一步的样本数以获得较高的计算效率,可以有多种准则.一种可选的方法是在每一步每隔若干样本监测一次系统中未死亡粒子属性分布对应的香农熵的收敛情况以决定何时停止追加样本,此种方法需要在每一步频繁计算香农熵值.由于在MPI消息传递并行编程环境下香农熵的经典计算方法必须广播大量的数据,导致每一步的计算时间随香农熵计算频率的提高而快速增大,这显然是不能满足实际需求的.本文提出了一种适应于消息传递并行编程环境的香农熵计算新方法,该方法计算得到的香农熵值并不等价于经典方法,但二者之间的差别会随着样本数的增加而趋于零.新方法的最大优势是高频计算香农熵值的时间代价大为降低,为最终实现基于香农熵收敛判断的每步样本数的自动调整奠定了必要的基础.     

非定常粒子输运蒙特卡罗并行算法研究

蒙特卡罗方法应用于粒子输运求解已是众所周知的事，然而由于MC的误差与跟踪的样本数的平方根成反比，通常要达到误差要求，需要模拟大量粒子历史，从而花费大量的计算时间。特别对非定常粒子输运问题的MC模拟，需要模拟上万个时间步，每步需要跟踪上百万个粒子。由于粒子寿命远超过时间步步长，因此，上个时间步剩余粒子的广播和本时间步剩余粒子状态量的汇总，I/O需求量大，成为制约并行的瓶颈。     

非定常输运模拟中基于粒子标识分类的源偏倚算法

在非定常输运问题的多步蒙特卡罗模拟中,根据粒子的不同属性进行标识分类可以得到非常细致的系统相关标识物理量.对于某些目标标识物理量,模拟的样本中造成有效贡献的样本相对较少,导致这些物理量模拟结果的涨落较大,而单靠增加总样本数不能高效地使有效样本达到一个合理的水平.本文提出一种基于标识分类的源偏倚算法,将增加的所有样本定向赋予目标类粒子,从而高效地降低目标标识物理量的统计误差且不影响非目标标识物理量的计算.以一维多层介质非定常输运模型验证上述结论.     

蒙特卡罗粒子输运问题的全局降方差方法

精确获得中子通量的时间-空间-能量精细分布对于一类非定常粒子输运蒙特卡罗模拟至关重要.以零方差理论为基础,采取化整为零的策略,把蒙特卡罗方法与离散纵标方法相耦合,用近似重要函数指导蒙特卡罗模拟,给出一种实现蒙特卡罗全局降方差的计算方案.数值算例表明,该计算方案获得了全局降方差的效果.     

多物理耦合计算中动态输运问题高效蒙特卡罗模拟方法

多物理耦合计算在众多领域都有重要应用.如果其包含粒子输运过程,用蒙特卡罗方法模拟粒子输运常占据大部分的计算时间,因此多物理耦合计算中动态输运问题的高效蒙特卡罗模拟方法意义重大,其不可避免地依赖于大规模并行.基于动态输运问题的特点,本文提出了两种新方法:一是针对输运燃耗耦合计算的新型计数规约算法;二是动态输运计算样本数自适应算法.两种算法都能在保持计算结果基本不变的前提下使计算时间大幅减少,从而提高了效率.     

高温热辐射输运模拟的蒙特卡罗全局降方差方法

针对隐式蒙特卡罗方法模拟高温热辐射输运问题时存在的“辐射强度计算误差时间空间分布严重不均匀”现象,通过理论分析和数值模拟手段,找到决定误差大小的主要因素为“网格内的辐射径迹长度记录数”.据此提出“隐式蒙特卡罗全局降方差方法”并推导相应的计算公式.该方法主要包含如下3个关键技术:1)针对辐射输运蒙特卡罗模拟的自适应动态分配源粒子方法; 2)与自适应动态分配源粒子相匹配的动态权窗设计技术及粒子权无偏估计算法; 3)辐射强度的解析估计降方差方法.针对这3个关键技术,设计蒙特卡罗数值模拟方案,编写相应的数值模拟程序模块.典型辐射输运问题模拟结果显示:隐式蒙特卡罗全局降方差方法能够使网格辐射强度计算误差在整个时空范围内分布相对比较均匀,最大误差可控,计算效率提升10倍左右.新方法在激光惯性约束聚变的黑腔辐射输运模拟应用中取得了显著效果.     

基于神经网络的粒子输运问题高效计算方法

蒙特卡罗方法是求解粒子输运问题的有力工具之一,其局限性在于为达到精度要求需模拟大量粒子,计算耗时长,这阻碍了该方法的进一步应用,尤其在需快速响应的情形.本文结合神经网络和若干蒙特卡罗方法基本原理发展了一种计算方法,能够实现源分布可变,几何、材料和目标计数不变的中子输运问题的快速准确求解.首先,为高效生成用于神经网络训练的数据,利用重要性原理实现在同样模拟次数基础上有效扩充训练数据集容量,在一定程度上克服了使用蒙特卡罗计算获取训练数据耗时长的缺点.进而,基于目标计数是源分布与重要性函数乘积积分的事实,设计了利用神经网络实现快速输运计算的策略.该网络的输入是中子源项,输出是目标计数,在几何、材料和目标计数固定的情况下,该神经网络可重复使用,根据新的源项快速准确得到目标计数.本文所提出方法的原理和框架同样适用于其他种类粒子的同类型输运问题.基于若干基准模型的验证表明,训练得到的神经网络能在不到1 s的时间内得到目标计数,且与蒙特卡罗大样本模拟得到基准结果的平均相对偏差均低于5%.     

用蒙特卡罗“测量”离散程序中的参数

为了快速、精确地计算高能X射线照相中的散射光子分布,提出了将该过程中的粒子输运问题近似为一个有效的纯光子输运问题。针对该纯光子输运方程提出了一种适合于计算机计算的逐级迭代求解公式,并将该公式进行了离散化,然后编写成了计算机离散程序。用蒙特卡罗程序MCNP模拟得到了该程序所需要的参数和分布函数。最后用MCNP对该程序进行了检验。对于薄客体离散程序的计算结果与MCNP符合较好,但对于厚客体二者有较大偏离。目前可以把该程序应用于一些定性的计算分析。     

非定常辐射输运问题的蒙特卡罗自适应偏倚抽样

用蒙特卡罗(MC)方法模拟高温、高压、多介质、大变形辐射输运问题时,由于网格体积悬殊,导致各网格通量的统计误差涨落很大,随着时间步的增加,误差积累甚至会导致计算结果失真.为此,发展了针对全局网格计算的源偏倚抽样技巧.用于源偏倚抽样的价值函数基于上个时间步各网格通量及误差,通过加权构造产生,它比传统MC通过解伴随方程获取价值的性价比要高得多.数值试验表明,全局源偏倚抽样通过自适应分配当前时间步各网格的粒子数,有效地降低了当前步重要网格通量误差. 关键词： 非定常 辐射输运 蒙特卡罗 源偏倚抽样     

纳柱阵列通道中生物分子等效淌度的宏观输运理论分析

各向异性生物分子或带电布朗粒子在周期性孔隙结构运动的分析在生物医学、水处理、环境工程等无数领域具有非常重要的意义. 本文基于宏观输运理论计算粒子在周期性微纳阵列结构中等效输运 参数, 预测分离结果. 首先通过引入构型熵及有效电荷等参数, 建立各向异性生物分子在纳米级受限环境下的等效布朗粒子模型, 然后应用宏观输运理论和数值方法计算分子的等效淌度. 以小分子DNA 片段在周期性纳柱阵列通道中电泳迁移为例, 证明当通道空隙接近或小于分子尺寸时, 熵受限对分子的等效迁移速度有重要的影响, 是实现生物分子分离的主要机理. 因为熵受限的作用随着外电场的增强而减低,所以在较低电场强度条件下, 分子淌度差别较大, 对应分离效果较佳. 关键词： 生物分子分离 构型熵 微纳阵列 宏观输运理论     

One-dimensional quantum walks with a position-dependent coin

We investigate the evolution of a discrete-time one-dimensional quantum walk driven by a position-dependent coin. The rotation angle, which depends upon the position of a quantum particle, parameterizes the coin operator. For different values of the rotation angle, we observe that such a coin leads to a variety of probability distributions, e.g. localized, periodic, classicallike, semi-classical-like, and quantum-like. Further, we study the Shannon entropy associated with position and the coin space of a quantum particle, and compare them with the case of the position-independent coin. Our results show that the entropy is smaller for most values of the rotation angle as compared to the case of the position-independent coin. We also study the effect of entanglement on the behavior of probability distribution and Shannon entropy by considering a quantum walk with two identical position-dependent entangled coins. We observe that in general, a wave function becomes more localized as compared to the case of the positionindependent coin and hence the corresponding Shannon entropy is lower. Our results show that a position-dependent coin can be used as a controlling tool of quantum walks.     

Investment strategies and hidden variables

The present study shows how the information on `hidden' market variables effects optimal investment strategies. We take the point of view of two investors, one who has access to the hidden variables and one who only knows the quotes of a given asset. Following Kelly's theory on investment strategies, the Shannon information and the doubling investment rate are quantified for both investors. Thanks to his privileged knowledge, the first investor can follow a better investment strategy. Nevertheless, the second investor can extract some of the hidden information looking at the past history of the asset variable. Unfortunately, due to the complexity of his strategy, this investor will have computational difficulties when he tries to apply it. He will than follow a simplified strategy, based only on the average sign of the last l quotes of the asset. This results have been tested with some Monte Carlo simulations.     

Entropy in quantum ratchet for a delta-kicked model

We investigate the evolution of Shannon entropy in quantum ratchet effect for a delta-kicked model, where a particle with initial momentum zero is periodically kicked by an asymmetric potential. It is shown that the evolution of Shannon entropy of the particle can remarkably reflect whether quantum resonance emerges and gives rise to ratchet current or not. Furthermore, for different kinds of quantum resonances, low-order or high-order quantum resonances, the evolutions of the entropy are quite different.     

Local entropy and structure in a two-dimensional frustrated system

We calculate the local contributions to the Shannon entropy and excess entropy and use these information theoretic measures as quantitative probes of the order arising from quenched disorder in the diluted Ising antiferromagnet on a triangular lattice. When one sublattice is sufficiently diluted, the system undergoes a temperature-driven phase transition, with the other two sublattices developing magnetizations of equal magnitude and opposite sign as the system is cooled.(1) The diluted sublattice has no net magnetization but exhibits spin glass ordering. The distribution of local entropies shows a dramatic broadening at low temperatures; this indicates that the system's total entropy is not shared equally across the lattice. The entropy contributions from some regions exhibit local reentrance, although the entropy of the system decreases monotonically as expected. The average excess entropy shows a sharp peak at the critical temperature, showing that the excess entropy is sensitive to the structural changes that occur as a result of the spin glass ordering.     

Uncertainty relations with quantum memory for the Wehrl entropy

We prove two new fundamental uncertainty relations with quantum memory for the Wehrl entropy. The first relation applies to the bipartite memory scenario. It determines the minimum conditional Wehrl entropy among all the quantum states with a given conditional von Neumann entropy and proves that this minimum is asymptotically achieved by a suitable sequence of quantum Gaussian states. The second relation applies to the tripartite memory scenario. It determines the minimum of the sum of the Wehrl entropy of a quantum state conditioned on the first memory quantum system with the Wehrl entropy of the same state conditioned on the second memory quantum system and proves that also this minimum is asymptotically achieved by a suitable sequence of quantum Gaussian states. The Wehrl entropy of a quantum state is the Shannon differential entropy of the outcome of a heterodyne measurement performed on the state. The heterodyne measurement is one of the main measurements in quantum optics and lies at the basis of one of the most promising protocols for quantum key distribution. These fundamental entropic uncertainty relations will be a valuable tool in quantum information and will, for example, find application in security proofs of quantum key distribution protocols in the asymptotic regime and in entanglement witnessing in quantum optics.     

Entropy description of a cooperation-competition system

Understanding the cooperation-competition dynamics is a long-standing challenge in studying complex systems.Inspired by the idea of Shannon entropy,we define competition information entropy and propose an entropy evolution model.The analytic results of the model of the relation between competition gain distribution parameters and entropy,as well as the relation between entropy and time are compared with empirical results obtained in 14 real world systems.They are found to be in good agreement with each other.     

Tomographic characteristics of spin states

Spin states are studied in the tomographic-probability representation. The standard probability distribution of spin projection onto a direction in space is used instead of the spinor or the density matrix to identify the quantum state. The Shannon entropy and information are associated with the spin tomographic probability. A short review of the probability-theory notions is presented. Analysis of tomographic entropy and tomographic information for the Werner state is considered. The probability representation is used to describe a spin-3/2 particle and two qubits. The connection of tomographic entropy with the von Neumann entropy is discussed.     

A Stepwise Assessment of Parsimony and Fuzzy Entropy in Species Distribution Modelling

Entropy is intrinsic to the geographical distribution of a biological species. A species distribution with higher entropy involves more uncertainty, i.e., is more gradually constrained by the environment. Species distribution modelling tries to yield models with low uncertainty but normally has to reduce uncertainty by increasing their complexity, which is detrimental for another desirable property of the models, parsimony. By modelling the distribution of 18 vertebrate species in mainland Spain, we show that entropy may be computed along the forward-backwards stepwise selection of variables in Logistic Regression Models to check whether uncertainty is reduced at each step. In general, a reduction of entropy was produced asymptotically at each step of the model. This asymptote could be used to distinguish the entropy attributable to the species distribution from that attributable to model misspecification. We discussed the use of fuzzy entropy for this end because it produces results that are commensurable between species and study areas. Using a stepwise approach and fuzzy entropy may be helpful to counterbalance the uncertainty and the complexity of the models. The model yielded at the step with the lowest fuzzy entropy combines the reduction of uncertainty with parsimony, which results in high efficiency.     

Application of permutation entropy method in the analysis of chaotic,noisy, and chaotic noisy series

We have considered a permutation entropy method for analyzing chaotic, noisy, and chaotic noisy series. We have introduced the concept of permutation entropy from a survey of some features of information entropy (Shannon entropy), described the algorithm for its calculation, and indicated the advantages of this approach in the analysis of time series; the application of this method in the analysis of various model systems and experimental data has also been demonstrated.