Departamento de Matemática Aplicada y Estadística, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros s/n, 28040 Madrid, Spain.
Centro Brasileiro de Pesquisas Fisicas, Rua Xavier Sigaud 150, Rio de Janeiro 22290-180, Brazil.
Phys Rev E. 2017 Oct;96(4-1):042158. doi: 10.1103/PhysRevE.96.042158. Epub 2017 Oct 30.
We numerically study the two-dimensional, area preserving, web map. When the map is governed by ergodic behavior, it is, as expected, correctly described by Boltzmann-Gibbs statistics, based on the additive entropic functional S_{BG}[p(x)]=-k∫dxp(x)lnp(x). In contrast, possible ergodicity breakdown and transitory sticky dynamical behavior drag the map into the realm of generalized q statistics, based on the nonadditive entropic functional S_{q}[p(x)]=k1-∫dx[p(x)]^{q}/q-1 (q∈R;S_{1}=S_{BG}). We statistically describe the system (probability distribution of the sum of successive iterates, sensitivity to the initial condition, and entropy production per unit time) for typical values of the parameter that controls the ergodicity of the map. For small (large) values of the external parameter K, we observe q-Gaussian distributions with q=1.935⋯ (Gaussian distributions), like for the standard map. In contrast, for intermediate values of K, we observe a different scenario, due to the fractal structure of the trajectories embedded in the chaotic sea. Long-standing non-Gaussian distributions are characterized in terms of the kurtosis and the box-counting dimension of chaotic sea.
我们对二维面积守恒的蛛网映射进行了数值研究。当映射受到遍历行为控制时,根据基于加性熵泛函$S_{BG}[p(x)]=-k∫dxp(x)lnp(x)$的玻尔兹曼-吉布斯统计,它可以得到正确的描述。相反,可能的遍历崩溃和过渡粘性动态行为会将映射拖入基于非加性熵泛函$S_{q}[p(x)]=k1-∫dx[p(x)]^{q}/q-1$的广义$q$统计的领域,其中$q\in R$;$S_{1}=S_{BG}$。我们针对控制映射遍历性的参数的典型值,对系统(连续迭代和初始条件的和的概率分布以及单位时间内的熵产生)进行了统计描述。对于外部参数$K$的小(大)值,我们观察到$q$=1.935⋯(高斯分布)的$q$-高斯分布(与标准映射一样)。相反,对于中间值的$K$,由于轨迹嵌入在混沌海中的分形结构,我们观察到了不同的情况。长期存在的非高斯分布可以根据混沌海的峰度和盒子计数维数来进行特征化。