Abstract
In this paper, we investigate functional inverse operators associated with a class of fractional integro-differential equations. We further explore the existence, uniqueness, and stability of solutions to a new integro-differential equation featuring variable coefficients and a functional boundary condition. To demonstrate the applicability of our main theorems, we provide several examples in which we compute values of the two-parameter Mittag–Leffler functions. The proposed approach is particularly effective for addressing a wide range of integral and fractional nonlinear differential equations with initial or boundary conditions—especially those involving variable coefficients, which are typically challenging to treat using classical integral transform methods. Finally, we demonstrate a significant application of the inverse operator approach by solving a Caputo fractional convection partial differential equation in with an initial condition.
Keywords:
fractional nonlinear integro-differential equation; uniqueness and existence; Banach’s contractive principle; Leray–Schauder’s fixed-point theorem; Mittag–Leffler function; functional inverse operator MSC:
34B15; 34A12; 34K20; 26A33
1. Introduction
The Riemann–Liouville fractional integral of order is defined for the function H [,] as
It follows that
The Liouville–Caputo fractional derivative of order of the function H is defined as []
It follows that
The set is a Banach space of all continuous functions from into with the norm
Let and be a functional. The purpose of the current work is to investigate the uniqueness, existence, and stability for the following new equation with variable coefficients for :
where and F are mappings from into with certain conditions.
In addition, we aim to find a new series solution to a Caputo fractional convection equation (PDE) in with an initial condition based on the inverse operator method at the end to show an application of the inverse operator method in fractional PDEs.
The two-parameter Mittag–Leffler function [] is defined by
where .
To demonstrate the use of the functional inverse operator approach, we first consider the following simpler version of Equation (1) for and :
where is a constant and F is a continuous function on with
We should point out that is nonlocal because it ties the value at to an integral over the entire domain. Generally speaking, it appears in problems involving global feedback, delayed responses, or non-instantaneous initialization, such as heat flow with memory, ecological systems with global resource limits, and models where sensors average data over a range before feedback.
Applying to Equation (2), we come to
by noting that
Hence,
To use the inverse operator method, we first define the functional operator over the space as
Then, is well-defined in . Since, for any , we have
Thus, is continuous over and the series
is uniformly convergent. We further prove that is an inverse operator of , namely
Clearly,
Moreover, is unique. In fact, assume is another operator satisfying
Then, we derive by applying to the above.
By Banach’s fixed-point theorem and the Mittag–Leffler function given above, we can use the implicit integral Equation (4) to find sufficient conditions for the uniqueness of Equation (2). To do so, we define a mapping over as
Then,
Thus, is a mapping from to itself.
Furthermore, we suppose that F satisfies the following Lipschitz condition for a constant :
and
Then, there is a unique solution in to Equation (2). We only need to show that is contractive. Clearly,
Therefore,
by noting that
Since , by Banach’s contractive principle, there exists a unique solution in to Equation (2).
In summary, we have the following result:
Theorem 1.
Let , and ω be a constant. We further assume that is a continuous and bounded function over with the condition for a constant :
and
Then, there is a unique solution in to Equation (2).
Example 1.
The equation with a nonlocal initial condition
has a unique solution in .
Proof.
From the equation, we get
and
is continuous and bounded over and satisfies
which claims that . From Theorem 1, we need to find the following value of :
using an online calculator from Wolfram Mathematica. Therefore, there is a unique solution. □
Stability is a key topic in differential equations []. Stability of a differential equation generally refers to how solutions behave over time in response to (a) initial conditions, and (b) perturbations (small changes in data, inputs, or forcing terms). There are different types of stability depending on context. Our stability concept is to study solutions that do not blow up due to small errors. The idea of such stability is the substitution of a differential equation with a given inequality that acts like a perturbation of the differential equation.
Definition 1.
Fractional differential equations are critical because they provide a more accurate and flexible framework for modeling real-world phenomena that exhibit memory, nonlocality, and anomalous behavior—features that classical (integer-order) differential equations cannot capture effectively. In many physical, biological, and engineering systems, the current state depends not only on the present but also on the entire history of the system. Fractional derivatives naturally incorporate memory due to their integral-based definitions (nonlocal properties). There are many interesting studies on fractional differential or integral equations due to their strong demands and applications in various pure and applied fields [,]. Metzler et al. [] used fractional relaxation regarding filled polymer networks and investigated the dependence of the decisive occurring parameters on the filler content. Momani and Odibat [] implemented analytical techniques, the variational iteration method and the Adomian decomposition method, for solving linear fractional partial differential equations arising in fluid mechanics. Dehghan and Shakeri [] presented the solution of ordinary differential equations with multi-point boundary value conditions by means of a semi-numerical approach, which is based on the homotopy analysis method, with engineering applications. In 2021, Li [] studied the uniqueness of solutions for certain partial integro-differential equations with the initial conditions in a Banach space using the inverse operator approach, convolution, and Banach’s contractive principle. Xu et al. [] employed the local fractional variational iteration method to obtain approximate analytical solution of the two-dimensional diffusion equation in fractal heat transfer with help of local fractional derivative and integral operators. In 2014, Cabada and Hamdi [] studied the existence of solutions of the following nonlinear fractional differential equations with integral boundary value conditions:
where and and is the Riemann–Liouville fractional derivative of order .
In 2018, Zhu [] investigated the existence and uniqueness of the following equation using the Henry–Gronwall integral inequalities:
Equation (1) is essentially important because it represents a new generalized fractional integro-differential boundary value problem that incorporates several key features found in complex real-world systems:
(a) It involves both a Caputo fractional derivative and fractional integrals as well as , which model memory effects and nonlocal behavior—critical in fields such as viscoelasticity, control theory, and diffusion processes.
(b) The presence of nonlinear functions and makes this equation more realistic for modeling physical and biological systems, where responses often depend nonlinearly on states or inputs.
(c) The variable coefficient introduces inhomogeneity, allowing the model to adapt to position-dependent properties or processes—important in heterogeneous media or materials with spatial variation.
(d) The boundary conditions are non-standard: At , the value depends on an integral condition involving . At , it depends on a functional , which could be nonlocal, nonlinear, or global in nature. These types of boundary conditions arise naturally in population dynamics, thermodynamics, economics, and fluid mechanics, where the state at a boundary is governed by average or cumulative behavior.
(e) From a theoretical perspective, studying existence, uniqueness, and stability for such an equation is nontrivial due to the combination of fractional operators, nonlinear terms, functional boundary conditions, and a mixed initial-boundary structure.
The motivation for employing the inverse operator method and Mittag–Leffler functions in the present work stems from the fact that, to the best of our knowledge, there are no existing integral transforms or alternative approaches capable of converting Equation (1) into an equivalent integral equation. However, to apply fixed-point theory for studying uniqueness and existence, such an equivalent integral formulation is essential in order to define a suitable nonlinear operator.
In the following sections, we derive an implicit integral equation that is equivalent to Equation (1). Then, by Banach’s contractive principle, a functional inverse operator, as well as the Mittag–Leffler function, we will obtain sufficient conditions for the uniqueness and stability. Furthermore, we study the existence based on Leray–Schauder’s fixed-point theorem. Also included are illustrative examples demonstrating applications of the main theorems. At the end, we find a series solution to a time-fractional convection equation in to show an application of the inverse operator method in PDEs.
2. Uniqueness and Stability
Theorem 2.
Let , be a functional, and . Furthermore, we assume that are continuous and bounded functions over . Then, Equation (1) is equivalent to the implicit integral equation in :
In addition, if
then
Proof.
Part I: Apply to Equation (1) to get
This claims
by setting . Evidently,
is a constant for , and, from , we get
Hence,
From Section 1, we can similarly show that the inverse operator of is
in the space . Indeed,
Moreover,
and V is unique.
So,
Furthermore,
Since
we get
Part II: To prove that given in Formula (6) is a solution of Equation (1), we see that V is a unique inverse operator of . This implies satisfies Equation (7), which is equivalent to Equation (6):
Using Lemma 2.21 (a) in [], we apply the operator to both sides of the above equation to get
Clearly, it satisfies the boundary conditions by reversing the above steps in Part I. This completes the proof. □
Regarding the uniqueness and stability, we have the theorem below.
Theorem 3.
Let , , and . Furthermore, we assume that are continuous and bounded functions over , with conditions
Let be a functional such that
If
then Equation (1) is stable with a unique solution in .
Proof.
To show the uniqueness, we use a mapping over the space , given by
From Theorem 2, the mapping is from to itself. We will prove that is contractive. For ,
This implies
Since , by Banach’s contractive principle, there is a unique solution in .
To prove the stability, we let
Then, and
Since is continuous and bounded, we have from Theorem 2
On the other hand, we get from the uniqueness of Equation (1) that
Then,
which deduces
where
is a stability constant, which is independent of . □
Example 2.
The equation with a variable coefficient
is stable and has a unique solution in .
Proof.
From the equation, we can see that
and
is a continuous and bounded function, and
which indicates . Similarly,
is a continuous and bounded function with
which claims , and
satisfies
which holds that . We need to evaluate the value of
By Theorem 3, Equation (9) is stable with a unique solution. □
3. Existence
We will prove the theorem about the existence of Equation (1) using Leray–Schauder’s fixed-point theorem.
Theorem 4.
Let , , and for all . Furthermore, we assume that are continuous and bounded functions over , and is a functional with the condition
for a nonnegative constant , and
Then, there is at least one solution to Equation (1).
Proof.
We use the mapping again, given by
From Theorem 2, is a mapping from to itself. For any , we have
which implies that (i) is continuous because F and are continuous.
(ii) is a mapping from bounded sets in to bounded sets. Assume is a bounded set in . For any ,
is bounded. Using
one claims the set
is bounded by noting that F and are bounded.
(iii) is equicontinuous over for . Let , and we have
We are going to prove is a Lipschitz function. Indeed,
Clearly,
by using
Let
for all . Then,
Hence,
by the proof above.
In summary,
which claims that is a Lipschitz function. Similarly, we can show all other for are also Lipschitz functions. Thus, is equicontinuous over for . This implies is a compact operator using the Arzela–Ascoli theorem.
(iv) Lastly, one needs to prove the set
is bounded. It holds since
is uniformly bounded from the proof of Theorem 2 and by using
again, where
We finish the proof. □
Remark 1.
(a) The conditions that is a Lipschitz function and in Theorem 4 are essential to prove the existence results as they are required in the Leray–Schauder fixed-point theorem.
(b) Theorem 3 requires that all , and are Lipschitz functions, while is only a Lipschitz function in Theorem 4. In addition, in Theorem 3 implies in Theorem 4. But, the converse is not true, obviously.
4. An Application
To complete this paper, we are going to provide a series solution to the following important Caputo fractional convection equation in for to demonstrate applications of inverse operators in PDEs:
where , for and
This equation with the initial condition is vital in applications based on the following factors:
(a) for introduces memory effects into the evolution of the system. This is crucial for accurately modeling anomalous transport, subdiffusion, and history-dependent processes. Classical convection equations (with integer derivatives) assume that the system’s rate of change depends only on the present, while this model assumes dependence on the entire history of the system.
(b) When , the equation reduces to the classical convection (or transport) equation:
which is widely used in fluid mechanics, traffic flow, and wave propagation.
(c) For , the equation captures subdiffusive dynamics, making it suitable for systems where transport is slower than classical models predict, such as in porous media, biological tissues, or financial markets.
(d) The term
represents convection or directional transport, which appears in heat transfer, fluid flow, and atmospheric dynamics.
To begin our process, we define the partial fractional integral of order as
Then, apply to Equation (10) to obtain
using
Thus,
We first claim that the inverse operator of is
in the subspace S of , given by
Clearly,
Letting
we have, for any ,
which implies that is well-defined.
In addition, is an inverse operator since
In fact,
and is unique.
Clearly, the solution given above is convergent in the space . Indeed,
In particular, for , we have
Evidently, if , then Equation (10) is
Example 3.
The equation for :
has a series solution
in .
Proof.
Clearly, , and
□
Remark 2.
There are many investigations on numerical solutions to various time-fractional convection equations; see [], for example. They are different from our inverse operator techniques and only find approximate solutions on finite domains in general, with or without convergence analysis. Solutions derived here are exact and well-defined in the space . However, it seems difficult and challenging to consider boundary value problems. In particular, if all , then
clearly is the solution satisfying the initial condition.
In addition, the inverse operator method can be broadly applied to the study of various fractional partial differential equations, such as the generalized multi-term time-fractional diffusion-wave and partial integro-differential equation [], the fractional convection–diffusion equation, and the generalized wave equation [].
Recently, Li and Wang [] applied the non-uniform L1/discontinuous Galerkin (DG) finite element method to study numerical solutions for the following two-dimensional problem for :
5. Conclusions
We have investigated the uniqueness, existence, and stability of the new Equation (1) using a functional inverse operator, the Mittag–Leffler function, and several fixed-point theorems in the Banach space . Multiple examples were presented to illustrate the applicability of the results. In addition, we applied the inverse operator method to solve the Caputo fractional convection Equation (10) in by introducing a newly constructed function space S. This represents a novel approach for analyzing a wide class of well-known partial differential equations—such as time-fractional diffusion equations involving the Laplacian operator Δ—through the framework of inverse operator techniques.
Author Contributions
Conceptualization, C.L., N.F. and Y.Y.O.; Methodology, C.L.; Software, C.L.; Validation, C.L., N.F. and Y.Y.O.; Formal Analysis, C.L., N.F. and Y.Y.O.; Writing—Original Draft Preparation, C.L.; Writing—Review & Editing, C.L., N.F. and Y.Y.O. All authors have read and agreed to the published version of the manuscript.
Funding
This research is supported by the Natural Sciences and Engineering Research Council of Canada (Grant No. 2019-03907).
Data Availability Statement
No new data were created or analyzed in this study. Data sharing is not applicable to this article.
Acknowledgments
The authors are thankful to the reviewers and editor for providing valuable comments and suggestions.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Kilbas, A.-A.; Srivastava, H.-M.; Trujillo, J.-J. Theory and Applications of Fractional Differential Equations; Elsevier: Amsterdam, The Netherlands, 2006. [Google Scholar]
- Samko, S.G.; Kilbas, A.A.; Marichev, O.I. Fractional Integrals and Derivatives: Theory and Applications; Gordon and Breach: London, UK, 1993. [Google Scholar]
- Hadid, S.-B.; Luchko, Y.-F. An operational method for solving fractional differential equations of an arbitrary real order. Panamer. Math. J. 1996, 6, 57–73. [Google Scholar]
- Li, C.; Liao, W. Applications of inverse operators to a fractional partial integro-differential equation and several well-known differential equations. Fractal Fract. 2025, 9, 200. [Google Scholar] [CrossRef]
- Hilfer, R. Applications of Fractional Calculus in Physics; World Scientific: Singapore, 2000. [Google Scholar]
- Podlubny, I. Fractional Differential Equations: An Introduction to Fractional Derivatives, Fractional Differential Equations, to Methods of Their Solution and Some of Their Applications; Academic Press: San Diego, CA, USA, 1999. [Google Scholar]
- Metzler, F.; Schick, W.; Kilian, H.G.; Nonnenmacher, T.F. Relaxation in filled polymers: A fractional calculus approach. J. Chem. Phys. 1995, 103, 7180–7186. [Google Scholar] [CrossRef]
- Momani, S.; Odibat, Z. Analytical approach to linear fractional partial differential equations arising in fluid mechanics. Phys. Lett. A 2006, 355, 271–279. [Google Scholar] [CrossRef]
- Dehghan, M.; Shakeri, F. A semi-numerical technique for solving the multi-point boundary value problems and engineering applications. Int. J. Numer. Methods Heat Fluid Flow 2011, 21, 794–809. [Google Scholar] [CrossRef]
- Li, C. Uniqueness of the partial integro-differential equations. J. Integral Equ. Appl. 2021, 22, 463–475. [Google Scholar] [CrossRef]
- Xu, S.; Ling, X.; Zhao, Y.; Jassim, H.K. A novel schedule for solving the two-dimensional diffusion in fractal heat transfer. Therm. Sci. 2015, 19, 99–103. [Google Scholar] [CrossRef]
- Cabada, A.; Hamdi, Z. Nonlinear fractional differential equations with integral boundary value conditions. Appl. Math. Comput. 2014, 228, 251–257. [Google Scholar] [CrossRef]
- Zhu, T. New Henry–Gronwall integral inequalities and their applications to fractional differential equations. Bull. Braz. Math. Soc. 2018, 49, 647–657. [Google Scholar] [CrossRef]
- Li, C.P.; Wang, Z. Non-uniform L1/discontinuous Galerkin approximation for the time-fractional convection equation with weak regular solution. Math. Comput. Simult. 2021, 182, 838–857. [Google Scholar] [CrossRef]
- Li, C. On boundary value problem of the nonlinear fractional partial integro-differential equation via inverse operators. Fract. Calc. Appl. Anal. 2025, 28, 386–410. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).