Abstract
This study presents findings on the existence, uniqueness, averaging principle, and numerical solutions for fractional stochastic systems influenced by both Brownian motion and Poisson jumps within the pth-moment framework. While most existing results for fractional stochastic differential equations are derived using the mean-square approach, this research offers results in the more general pth-moment framework, enhancing applicability. The results are derived using the -Hilfer fractional derivative, a generalized operator defined in relation to another function. This operator enables the memory effect to vary according to a nonlinear time scale. The main motivation behind this work is that there is no research work on -Hilfer fractional stochastic systems with standard Brownian motion and Poisson jumps regarding existence, uniqueness, and averaging principles in the pth-moment.
MSC:
26A33; 60H10; 34K50; 65C30
1. Introduction
The -Hilfer fractional derivative (-HFrD) is a generalized fractional operator defined with respect to another function , allowing the memory effect to depend on a nonlinear time scale. It unifies several fractional derivatives, including the Riemann–Liouville and Caputo types, and provides a flexible framework for modeling systems with nonlocal and hereditary behavior.
Definition 1.
Let and be strictly increasing. For , the left-sided fractional integral of order η with respect to γ is defined by [1]
Remark 1.
The above operator generalizes the classical Riemann–Liouville fractional integral by replacing the standard time variable with a nonlinear function .
Definition 2.
Let be such that , and let be strictly increasing. For , the left-sided Caputo-type fractional derivative of order η with respect to γ is defined by [2]
where
Remark 2.
The operator represents differentiation with respect to the function rather than the standard variable t. Hence, the above derivative can be interpreted as a Caputo-type fractional operator defined on a nonlinear time scale.
Definition 3.
Let satisfy and let . Under the same assumptions on γ, the left-sided Hilfer-type fractional derivative of order η and type ϑ with respect to γ is defined by [3]
Remark 3.
The parameter interpolates between different types of fractional derivatives. Specifically, corresponds to the γ-Riemann–Liouville type derivative, while yields the γ-Caputo type derivative.
Remark 4.
For the fundamental case (i.e., ), the above operators simplify significantly. In particular, the γ-Hilfer fractional derivative reduces to
This case is frequently used in applications and highlights the role of the parameter ϑ.
Remark 5.
If , then all the above operators reduce to the classical Riemann–Liouville, Caputo, and Hilfer fractional derivatives.
Lemma 1.
Let , with and . Define [3]
Then the following composition formula holds:
Remark 6.
The above result establishes the inverse relationship between the γ-fractional integral and the γ-Hilfer derivative. It shows that the fractional integral of the Hilfer-type derivative recovers the original function up to correction terms involving generalized initial values.
Fractional stochastic differential equations (FSDEs) that incorporate both Brownian motion and Poisson jump noise offer an advanced framework for modeling systems that exhibit memory effects and stochastic fluctuations. In this context, the Brownian motion component accounts for continuous, minor fluctuations, while the compound Poisson process addresses significant, sudden shocks of varying magnitudes. This integrated approach serves as a powerful mathematical tool for understanding systems affected by the interaction of non-Markovian memory, diffuse randomness, and abrupt changes.
Fractional stochastic systems driven by standard Brownian motion and Poisson jumps have numerous practical applications, particularly in economics. They can effectively model economic growth processes that exhibit long-memory dynamics, wherein past investment decisions and shocks continuously affect present output. Poisson jumps highlight the speed of economic changes in response to policy shifts, financial crises, or market disruptions. These models also enable monitoring of inflation and interest rate fluctuations over time. For instance, they illustrate gradual changes in rates that can accelerate dramatically due to global events or government monetary announcements. In labor economics, these systems reflect variations in job markets over time, influenced by long-term trends as well as sudden layoffs or hiring bursts. Financial institutions, including banks, utilize fractional stochastic models to assess how enduring trends and sudden shocks, such as credit defaults or liquidity crises, will impact their operations. Such models enhance our understanding of dynamic economies that are subject to continuous change and significant, abrupt events.
Existence and uniqueness (Ex-Un) of solutions are fundamental principles in any mathematical model, ensuring that the system’s behavior is distinctly characterized and predictable. Establishing existence confirms the existence of a solution to the system, while uniqueness ensures that this solution is the sole one that aligns with the specified initial conditions. This clarity keeps system dynamics from being unclear, which is important for both theoretical analysis and numerical simulations.
The averaging principle (Av-Pr) for FSDEs provides a structured methodology for comprehending the temporal dynamics of intricate systems, especially concerning memory effects and stochastic perturbations. This principle helps us describe the system more accurately because it averages out terms that change quickly. It keeps important features of both fractional-order memory and stochastic events, like Poisson jumps and Brownian motion. By applying this concept to FSDEs, we can gain insights into the temporal evolution of systems influenced by stochastic processes and fractional dynamics. It also gives you a good starting point for making reduced-order models that keep important statistical and dynamic properties. This method is very useful for studying systems that are hard to study directly or to simulate numerically in the full fractional stochastic context.
As the Ex-Un of solutions is an important issue in the theory of FSDEs. In their investigation of neutral FSDEs with infinite delay, Sakthivel et al. [4] discovered stability properties and existence results. More generally, Ex-Un results for nonlinear FSDEs driven by Brownian motion have been obtained by combining fixed-point arguments with stochastic analytical methods; see, for instance [5,6,7,8]. These papers draw attention to the extra mathematical challenges brought about by the interaction of stochastic disturbances and fractional memory.
Numerous stability concepts for FSDEs have been thoroughly examined in addition to well-posedness. Particularly crucial for comprehending how sensitive stochastic systems are to perturbations are mean-square stability and higher-moment stability. Refs. [9,10,11,12] developed adequate criteria guaranteeing mean-square and higher-moment stability for a number of fractional stochastic model classes in this way.
In recent years, the Av-Pr and controllability have also attracted significant attention. Several notable results concerning averaging for fractional stochastic systems were obtained in [13,14,15]. Likewise, controllability properties of FSDEs, especially for systems involving delays, neutral terms, and nonlocal conditions, have been investigated extensively; see [16,17].
The following is a summary of the main contributions of this study:
- To the best of my knowledge, there is no research on -Hilfer fractional stochastic systems with standard Brownian motion and Poisson jumps that addresses existence, uniqueness, and Av-Pr in the pth moment.
- Most existing results on existence, uniqueness, and the Av-Pr for fractional stochastic systems have been established under standard Brownian motion. In this work, we extend these results to systems driven by both Brownian motion and Poisson jumps.
- Most existing results for FSDEs are derived in the mean-square sense. In contrast, this work presents results in the more general pth moment, providing broader applicability.
- All results are based on the -HFrD, a generalized fractional operator that encompasses several fractional operators.
We study the following FSDEs with delay:
here, denotes the state vector, modeled as a stochastic process. denotes the -HFrD of order with parameter . is the delay time, and is continuous.
The following mappings are measurable and continuous:
Furthermore, denotes an -dimensional Brownian motion defined over the probability space . Consider as a -finite measurable space. Denote by the counting measure of a stationary Poisson point process on . The compensated Poisson random measure is defined as
The paper is structured as follows: Section 2 presents essential preliminaries on fractional calculus, stochastic analysis, and functional spaces. Section 3 addresses the Ex-Un of the proposed system. The Av-Pr is investigated in Section 4. Numerical approximations and simulation results based on a Euler–Maruyama approach are provided in Section 5. Finally, concluding remarks and potential directions for future research are discussed in Section 6 and Section 7, respectively.
2. Preliminaries
This section collects the fundamental definitions, notation, and hypotheses required for the subsequent analysis.
Definition 4.
We denote by the space of -measurable, a-integrable functions satisfying
where denotes the expectation with respect to .
A stochastic process that satisfies the required measurability conditions is called -adapted if for every . For any initial value , such an -adapted process is a solution of (3) whenever condition (4) holds.
For , , and , we make the following assumptions:
When there are , , and such as
For all ,
The terms , , and are essentially bounded in time, which implies that
We now propose several conditions that form the foundation of the Av-Pr.
- For any and , there exist constants such that
Lemma 2.
Assume that there exist real numbers with such that for all . Then, for every , the following inequality holds:
Lemma 3.
Assume that and . Then Hölder’s inequality is given by
where .
Lemma 4
([18]). Let and assume that
Consequently, one can find a constant for which
3. Existence and Uniqueness
By applying the Banach contraction principle (BCP), we first establish the Ex-Un results for solutions of FSDEs with respect to the -HFrD in the p-th moment. The space is defined as the set of all measurable and -adapted processes such that
It is straightforward to verify that is a Banach space. The next step is to define an operator such that , and
Lemma 5.
Given that and hold, the operator ℑ is well defined.
Proof.
Suppose for all . Then, by applying Equation (5) and Lemma 2, we have
□
Lemma 3 yields the following result:
where .
By invoking , Equation (10) yields the following:
Applying Burkholder–Davis–Gundy Inequality (BDG-I) and Lemma 3, we obtain
where and .
Under assumptions and , we have the following:
Thus, , we get the following:
From Equations (14) and (12),
From Lemma 2, assumptions and , and Lemma 4, it follows that:
here .
By employing Equations (11), (15) and (16) in (6), we obtain . Therefore, the mapping is well defined.
Next, we present an important lemma that plays a crucial role in proving the theorem on the Ex-Un of the solution to Equation (3).
Lemma 6.
For any , and , we have
where
is the Gamma function.
Proof.
Let be arbitrary. Consider the following fractional differential equation involving the -HFrD of the form:
□
Therefore, the following equality holds:
Hence
In the next theorem, the Ex-Un of solutions are demonstrated by showing that the operator ℑ is contractive under an appropriately weighted norm. In this context, the MLF
serves as the weight function.
Theorem 1.
Proof.
First, choose a fixed such that
where
□
We now define a weighted norm on the space as
The norms and are equivalent. Therefore, the space is complete.
To prove that ℑ is a contraction mapping, let . From Equation (4) and the bounds established in Lemma 2, we obtain:
Using the Lemma 3 and , we obtain
However, by applying and the BHD-I, we deduce:
By applying Lemma 2, assumptions and , together with Lemma 4, we obtain the following:
For , one has
For :
- Typically for (or known initial conditions)
- Thus
Perform change of variables:
Thus, from Equation (29), we obtain
Therefore, from Equation (27), it follows that
Therefore, Equation (28) implies that
From Equation (26), it follows that
Here, is given in Equation (24). This result indicates that, by using the definition of from Equation (25),
Application of Lemma 6 yields the following result:
From (38), we obtain
where
By (22), we have
Since , it follows that , and hence
Therefore, the operator ℑ is a contraction on the .
By the BCP, ℑ admits a unique fixed point in , which is the unique solution of Equation (3).
4. Averaging Principle
We now present the growth conditions for and prove the Av-Pr result for FSDEs.
Lemma 7.
If and hold, then for any , we have
and
where
and denotes a bound on the time-oscillation of over the interval .
Proof.
It can be deduced from , , and Lemma 2 that
which yields the desired bound in the following form:
In a similar manner, we can show that
Subsequently, we present the time-scale change property for the -HFrD. □
Lemma 8.
[Time-scale change property for the γ-HFrD] Let and . Assume that is strictly increasing with and satisfies the homogeneity condition
for some .
Let f be such that the expressions below are well-defined and define
Then for all ,
Proof.
We recall the -Riemann–Liouville fractional integral:
The -HfrD of order and type is defined by
Denote the operator
Step 1: From the homogeneity assumption , differentiating with respect to t yields
Moreover,
Step 2: Let and . Then
Using the substitution (so , ), and the homogeneity properties above, we obtain
Step 3: For any smooth function h,
Step 4: Let
Then
Using Step 2 with order a,
Applying Step 3,
Applying Step 2 again with order b,
Since , we have
Therefore,
This completes the proof. □
Next, we establish a result for the Av-Pr of FSDEs driven by standard Brownian motion and Poisson jumps for second case.
When we set and apply Lemma 8, Equation (40) yields the following:
By considering and representing and , we get
Without loss of generality, we may set . Then Equation (40) can be written in standard form by applying the natural time scaling .
Thus, Equation (41) can be written in integral form as
such that is a small positive parameter, with fixed. This leads to the following averaged formulation of Equation (42):
where , , and .
Theorem 2.
Suppose and hold. Then, for , , and , we have
Proof.
By combining the BDG-I, (), Lemma 3, and Doob’s martingale inequality, we conclude that
where
Using Equations (42) and (43), we get
By Lemma 2, we have
For any , taking expectations on both sides of Equation (46), we obtain
From Lemma 2, it follows that
By Lemma 3 and (), we obtain
where .
- In view of Lemma 3, we deduce that
- For the second term, , we have
Applying Lemma 7 and using an estimation method similar to that in Equation (54), we obtain
where
Next, we consider the third term. Using an argument similar to that in Equation (53), we obtain
From Lemma 4, we have
Again, from the second part of Equation (57), we obtain
In addition, we have
By Lemma 2, () and (), and Lemma 3, we deduce that:
and
Substituting Equations (62) and (63) into Equation (61), we obtain
where
Therefore, the following result holds:
where
and
Assume the following:
Considering that
then
Equation (65) therefore leads to
Then,
It follows that
Applying Gronwall’s inequality,
Additionally, we establish
Fix and . Then, for , we obtain
where
and
Therefore, the following holds:
□
5. Application
In this section, we introduce a population model that is governed by a -Hilfer fractional stochastic delay differential equation.
We consider the following system
with the initial history condition
where is a given continuous function.
The assumptions of the Theorem 1 are clearly satisfied, which indicates that the system Equation (66) has a solution. We applied the Euler–Maruyama method to solve the system Equation (66), and the results are shown.
Figure 1 and Figure 2 present numerical simulations of the -Hilfer fractional stochastic population model with delay and Poisson jumps. Figure 1 illustrates a representative trajectory of the population process, while Figure 2 displays the corresponding jump increments generated by the compound Poisson process.
Figure 1.
Sample trajectory of the -Hilfer fractional population model with delay and Poisson jumps; dashed lines mark jump events.
Figure 2.
Compound Poisson jump increments indicating the timing and magnitude of random shocks.
Figure 3 and Figure 4 present numerical simulations of the -Hilfer fractional stochastic population model with delay and Poisson jumps. Figure 3 shows five independent sample paths together with their mean trajectory, illustrating the combined effects of memory, delay, diffusion, and jump disturbances. Figure 4 depicts the corresponding mean evolution of the population process.
Figure 3.
Five sample paths of the -Hilfer fractional population model with their mean trajectory.
Figure 4.
Mean trajectory of the -Hilfer fractional stochastic population model over five simulations.
6. Conclusions
Our considered system incorporates both Brownian motion and Poisson jumps, making the direct application of the BCP considerably more challenging compared to its use in FSDEs driven solely by standard Brownian motion. Therefore, we employ the BCP within this more complex framework to address the additional jump components effectively. Similarly, establishing the results of the averaging principle for FSDEs involving standard Brownian motion and Poisson jumps is more difficult than for systems influenced only by Brownian motion.
We made significant contributions to establishing the results for both Ex-Un and Av-Pr. First, by deriving results for the p-th moment, we expanded the findings beyond the specific case of . Second, we established these results within the context of the -HFrD. Third, we addressed FSDEs driven by both Brownian motion and Poisson jumps, thus covering a wider class of stochastic systems.
7. Future Directions
Future research may focus on extending the present analysis to systems driven by more general noise processes, impulsive effects, distributed delays, or control and optimization problems associated with fractional stochastic delay systems.
Author Contributions
Conceptualization, M.I.L. and R.M.H.; Methodology, M.I.L. and R.M.H.; Software, M.I.L. and R.M.H.; Validation, M.I.L. and R.M.H.; Formal analysis, M.I.L. and R.M.H.; Investigation, M.I.L. and R.M.H.; Resources, R.M.H.; Writing—original draft, M.I.L. and R.M.H.; Writing—review & editing, M.I.L. and R.M.H.; Visualization, M.I.L. and R.M.H.; Funding acquisition, R.M.H. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2604).
Data Availability Statement
No data were generated or analyzed during the current study.
Conflicts of Interest
The authors declare no conflicts of interest.
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