Space-Time Inversion of Stochastic Dynamics

• Main Authors: Massimiliano Giona, Antonio Brasiello, Alessandra Adrover
• Format: Article
• Language: English
• Published: MDPI AG 2020-05-01
• Series: Symmetry
• Subjects: stochastic processes; space-time inversion; poisson-kac processes; stochastic stieltjes integrals; transit-time statistics; fractal time
• Online Access: https://www.mdpi.com/2073-8994/12/5/839

This article introduces the concept of space-time inversion of stochastic Langevin equations as a way of transforming the parametrization of the dynamics from time to a monotonically varying spatial coordinate. A typical physical problem in which this approach can be fruitfully used is the analysis of solute dispersion in long straight tubes (Taylor-Aris dispersion), where the time-parametrization of the dynamics is recast in that of the axial coordinate. This allows the connection between the analysis of the forward (in time) evolution of the process and that of its exit-time statistics. The derivation of the Fokker-Planck equation for the inverted dynamics requires attention: it can be deduced using a mollified approach of the Wiener perturbations “a-la Wong-Zakai” by considering a sequence of almost everywhere smooth stochastic processes (in the present case, Poisson-Kac processes), converging to the Wiener processes in some limit (the Kac limit). The mathematical interpretation of the resulting Fokker-Planck equation can be obtained by introducing a new way of considering the stochastic integrals over the increments of a Wiener process, referred to as stochastic Stjelties integrals of mixed order. Several examples ranging from stochastic thermodynamics and fractal-time models are also analyzed.