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Regularized fractional Ornstein-Uhlenbeck processes and their relevance to the modeling of fluid turbulence.

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Motivated by the modeling of the temporal structure of the velocity field in a highly turbulent flow, we propose and study a linear stochastic differential equation that involves the ingredients… Click to show full abstract

Motivated by the modeling of the temporal structure of the velocity field in a highly turbulent flow, we propose and study a linear stochastic differential equation that involves the ingredients of an Ornstein-Uhlenbeck process, supplemented by a fractional Gaussian noise, of parameter H, regularized over a (small) time scale ε>0. A peculiar correlation between these two plays a key role in the establishment of the statistical properties of its solution. We show that this solution reaches a stationary regime, which marginals, including variance and increment variance, remain bounded when ε→0. In particular, in this limit, for any H∈]0,1[, we show that the increment variance behaves at small scales as the one of a fractional Brownian motion of same parameter H. From the theoretical side, this approach appears especially well suited to deal with the (very) rough case H<1/2, including the boundary value H=0, and to design simple and efficient numerical simulations.

Keywords: uhlenbeck processes; relevance modeling; ornstein uhlenbeck; fractional ornstein; regularized fractional; processes relevance

Journal Title: Physical Review E
Year Published: 2017

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