Operational solution for the generalized Fokker-Planck and generalized diffusion-wave equations

The evolution operator method is used to solve the generalized Fokker-Planck equations and the generalized diffusion-wave equations in the (1+1)-dimensional space in which x∈R and t∈R_{+}. These equations contain either the first- or the second-time derivatives smeared by memory functions, each of which forms an integral kernel [denoted by f(ξ,t), ξ∈R_{+}] of suitable evolution operators. If memory functions in the Laplace space are Stieltjes functions, then f(ξ,t) satisfies normalization, non-negativity, and infinite divisibility to be considered a probability density function. The evolution operators also contain exponential-like operators whose action on initial condition p_{0}(x)>0 leads to the parent process distribution functions. This makes the results fully analogous to those obtained within the standard subordination approach. The above conclusion is satisfied by the solution of the generalized Fokker-Planck equation. In the case of the generalized diffusion-wave equation, to get this property, we should employ a special class, namely, "diffusion-like" initial conditions. The key models of the operator method involve power-law memory functions. It leads to the characterization of f(ξ,t) by applying one-sided stable Lévy distributions. The paper also examines the properties of evolution operators in terms of evolution and self-reproduction.

Authors

Institutions

Publication Details

Journal
Physical review. E
Published
2025-02-03
DOI
https://doi.org/10.1103/physreve.111.024103
Citations
1
Primary Topic
Fractional Differential Equations Solutions
Type
article
Field-Weighted Citation Impact
1.37

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Operational solution for the generalized Fokker-Planck and generalized diffusion-wave equations

K. Górska
1 citations
Physical review. E
Fractional Differential Equations Solutions
1.37
article

Operational solution for the generalized Fokker-Planck and generalized diffusion-wave equations

K. Górska
article en
1 citations

Abstract

The evolution operator method is used to solve the generalized Fokker-Planck equations and the generalized diffusion-wave equations in the (1+1)-dimensional space in which x∈R and t∈R_{+}. These equations contain either the first- or the second-time derivatives smeared by memory functions, each of which forms an integral kernel [denoted by f(ξ,t), ξ∈R_{+}] of suitable evolution operators. If memory functions in the Laplace space are Stieltjes functions, then f(ξ,t) satisfies normalization, non-negativity, and infinite divisibility to be considered a probability density function. The evolution operators also contain exponential-like operators whose action on initial condition p_{0}(x)>0 leads to the parent process distribution functions. This makes the results fully analogous to those obtained within the standard subordination approach. The above conclusion is satisfied by the solution of the generalized Fokker-Planck equation. In the case of the generalized diffusion-wave equation, to get this property, we should employ a special class, namely, "diffusion-like" initial conditions. The key models of the operator method involve power-law memory functions. It leads to the characterization of f(ξ,t) by applying one-sided stable Lévy distributions. The paper also examines the properties of evolution operators in terms of evolution and self-reproduction.

Physical review. EVol. 111(2)
Institute of Nuclear Physics, Polish Academy of Sciences (PL)
Narodowe Centrum Nauki
Openalex Percentile: Top 26%
Fractional Differential Equations Solutions
1.37
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.