Non-Markovian diffusion equations and processes: analysis and simulations

TL;DR

This paper introduces and analyzes non-Markovian diffusion equations driven by memory kernels, exploring their properties, solutions, and stochastic simulations, to better understand complex systems with memory effects.

Contribution

It presents a novel class of non-Markovian diffusion equations linked to stochastic processes with memory, including exact solutions and simulation methods.

Findings

Derived exact solutions for non-Markovian diffusion equations

Developed stochastic simulation algorithms for the models

Analyzed the impact of memory kernels on diffusion behavior

Abstract

In this paper we introduce and analyze a class of diffusion type equations related to certain non-Markovian stochastic processes. We start from the forward drift equation which is made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation can be interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the memory kernel K(t). We develop several applications and derive the exact solutions. We consider different stochastic models for the given equations providing path simulations.