TY - JOUR A1 - Sposini, Vittoria A1 - Chechkin, Aleksei V. A1 - Metzler, Ralf T1 - First passage statistics for diffusing diffusivity JF - Journal of physics : A, Mathematical and theoretical N2 - A rapidly increasing number of systems is identified in which the stochastic motion of tracer particles follows the Brownian law < r(2)(t)> similar or equal to Dt yet the distribution of particle displacements is strongly non-Gaussian. A central approach to describe this effect is the diffusing diffusivity (DD) model in which the diffusion coefficient itself is a stochastic quantity, mimicking heterogeneities of the environment encountered by the tracer particle on its path. We here quantify in terms of analytical and numerical approaches the first passage behaviour of the DD model. We observe significant modifications compared to Brownian-Gaussian diffusion, in particular that the DD model may have a faster first passage dynamics. Moreover we find a universal crossover point of the survival probability independent of the initial condition. KW - diffusion KW - superstatistics KW - first passage Y1 - 2018 U6 - https://doi.org/10.1088/1751-8121/aaf6ff SN - 1751-8113 SN - 1751-8121 VL - 52 IS - 4 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Vitali, Silvia A1 - Sposini, Vittoria A1 - Sliusarenko, Oleksii A1 - Paradisi, Paolo A1 - Castellani, Gastone A1 - Pagnini, Gianni T1 - Langevin equation in complex media and anomalous diffusion JF - Interface : journal of the Royal Society N2 - The problem of biological motion is a very intriguing and topical issue. Many efforts are being focused on the development of novel modelling approaches for the description of anomalous diffusion in biological systems, such as the very complex and heterogeneous cell environment. Nevertheless, many questions are still open, such as the joint manifestation of statistical features in agreement with different models that can also be somewhat alternative to each other, e.g. continuous time random walk and fractional Brownian motion. To overcome these limitations, we propose a stochastic diffusion model with additive noise and linear friction force (linear Langevin equation), thus involving the explicit modelling of velocity dynamics. The complexity of the medium is parametrized via a population of intensity parameters (relaxation time and diffusivity of velocity), thus introducing an additional randomness, in addition to white noise, in the particle's dynamics. We prove that, for proper distributions of these parameters, we can get both Gaussian anomalous diffusion, fractional diffusion and its generalizations. KW - anomalous diffusion KW - heterogeneous media KW - biological transport KW - Gaussian processes KW - space-time fractional diffusion equation KW - fractional Brownian motion Y1 - 2018 U6 - https://doi.org/10.1098/rsif.2018.0282 SN - 1742-5689 SN - 1742-5662 VL - 15 IS - 145 PB - Royal Society CY - London ER - TY - JOUR A1 - Sposini, Vittoria A1 - Chechkin, Aleksei V. A1 - Seno, Flavio A1 - Pagnini, Gianni A1 - Metzler, Ralf T1 - Random diffusivity from stochastic equations BT - comparison of two models for Brownian yet non-Gaussian diffusion JF - New Journal of Physics N2 - A considerable number of systems have recently been reported in which Brownian yet non-Gaussian dynamics was observed. These are processes characterised by a linear growth in time of the mean squared displacement, yet the probability density function of the particle displacement is distinctly non-Gaussian, and often of exponential(Laplace) shape. This apparently ubiquitous behaviour observed in very different physical systems has been interpreted as resulting from diffusion in inhomogeneous environments and mathematically represented through a variable, stochastic diffusion coefficient. Indeed different models describing a fluctuating diffusivity have been studied. Here we present a new view of the stochastic basis describing time dependent random diffusivities within a broad spectrum of distributions. Concretely, our study is based on the very generic class of the generalised Gamma distribution. Two models for the particle spreading in such random diffusivity settings are studied. The first belongs to the class of generalised grey Brownian motion while the second follows from the idea of diffusing diffusivities. The two processes exhibit significant characteristics which reproduce experimental results from different biological and physical systems. We promote these two physical models for the description of stochastic particle motion in complex environments. Y1 - 2018 U6 - https://doi.org/10.1088/1367-2630/aab696 SN - 1367-2630 SP - 1 EP - 33 PB - Deutsche Physikalische Gesellschaft / Institute of Physics CY - Bad Honnef und London ER - TY - GEN A1 - Sposini, Vittoria A1 - Chechkin, Aleksei V. A1 - Flavio, Seno A1 - Pagnini, Gianni A1 - Metzler, Ralf T1 - Random diffusivity from stochastic equations BT - comparison of two models for Brownian yet non-Gaussian diffusion T2 - New Journal of Physics N2 - Brownian yet non-Gaussian dynamics was observed. These are processes characterised by a linear growth in time of the mean squared displacement, yet the probability density function of the particle displacement is distinctly non-Gaussian, and often of exponential(Laplace) shape. This apparently ubiquitous behaviour observed in very different physical systems has been interpreted as resulting from diffusion in inhomogeneous environments and mathematically represented through a variable, stochastic diffusion coefficient. Indeed different models describing a fluctuating diffusivity have been studied. Here we present a new view of the stochastic basis describing time dependent random diffusivities within a broad spectrum of distributions. Concretely, our study is based on the very generic class of the generalised Gamma distribution. Two models for the particle spreading in such random diffusivity settings are studied. The first belongs to the class of generalised grey Brownian motion while the second follows from the idea of diffusing diffusivities. The two processes exhibit significant characteristics which reproduce experimental results from different biological and physical systems. We promote these two physical models for the description of stochastic particle motion in complex environments. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 416 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-409743 ER -