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Self-similar stochastic models with stationary increments for symmetric space-time fractional diffusion

机译:具有平稳增量的平稳时空分数扩散的自相似随机模型

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An approach to develop stochastic models for studying anomalous diffusion is proposed. In particular, in this approach the stochastic particle trajectory is based on the fractional Brownian motion but, for any realization, it is multiplied by an independent random variable properly distributed. The resulting probability density function for particle displacement can be represented by an integral formula of subordination type and, in the single-point case, it emerges to be equal to the solution of the spatially symmetric space-time fractional diffusion equation. Due to the fractional Brownian motion, this class of stochastic processes is self-similar with stationary increments in nature and uniquely defined by the mean and the auto-covariance structure analogously to the Gaussian processes. Special cases are the time-fractional diffusion, the space-fractional diffusion and the classical Gaussian diffusion.
机译:提出了一种开发用于研究异常扩散的随机模型的方法。特别地,在这种方法中,随机粒子轨迹基于分数布朗运动,但是对于任何实现,它都乘以一个适当分布的独立随机变量。所得的粒子位移概率密度函数可以由从属类型的积分公式表示,在单点情况下,它等于空间对称时空分数扩散方程的解。由于分数布朗运动,这类随机过程在自然界具有固定的增量,因此具有自相似性,并且类似于高斯过程,由均值和自协方差结构唯一地定义。特殊情况是时间分数扩散,空间分数扩散和经典高斯扩散。

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