Extended Poisson-Kac theory: A unifying framework for stochastic processes with finite propagation velocity

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Abstract

Stochastic processes play a key role for modeling a huge variety of transport problems out of equilibrium, with manifold applications throughout the natural and social sciences. To formulate models of stochastic dynamics, the conventional approach consists in superimposing random fluctuations on a suitable deterministic evolution. These fluctuations are sampled from probability distributions that are prescribed a priori, most commonly as Gaussian or Lévy. While these distributions are motivated by (generalized) central limit theorems, they are nevertheless unbounded, meaning that arbitrarily large fluctuations can be obtained with finite probability. This property implies the violation of fundamental physical principles such as special relativity and may yield divergencies for basic physical quantities like energy. Here, we solve the fundamental problem of unbounded random fluctuations by constructing a comprehensive theoretical framework of stochastic processes possessing physically realistic finite propagation velocity. Our approach is motivated by the theory of Lévy walks, which we embed into an extension of conventional Poisson-Kac processes. The resulting extended theory employs generalized transition rates to model subtle microscopic dynamics, which reproduces nontrivial spatiotemporal correlations on macroscopic scales. It thus enables the modeling of many different kinds of dynamical features, as we demonstrate by three physically and biologically motivated examples. The corresponding stochastic models capture the whole spectrum of diffusive dynamics from normal to anomalous diffusion, including the striking "Brownian yet non-Gaussian"diffusion, and more sophisticated phenomena such as senescence. Extended Poisson-Kac theory can, therefore, be used to model a wide range of finite-velocity dynamical phenomena that are observed experimentally.