- Research
- Open Access
- Published:
Higher-dimensional physical models with multimemory indices: analytic solution and convergence analysis
Advances in Difference Equations volume 2020, Article number: 364 (2020)
Abstract
The purpose of this work is to analytically simulate the mutual impact for the existence of both temporal and spatial Caputo fractional derivative parameters in higher-dimensional physical models. For this purpose, we employ the γ̅-Maclaurin series along with an amendment of the power series technique. To supplement our idea, we present the necessary convergence analysis regarding the γ̅-Maclaurin series. As for the application side, we solved versions of the higher-dimensional heat and wave models with spatial and temporal Caputo fractional derivatives in terms of a rapidly convergent γ̅-Maclaurin series. The method performed extremely well, and the projections of the obtained solutions into the integer space are compatible with solutions available in the literature. Finally, the graphical analysis showed a possibility that the Caputo fractional derivatives reflect some memory characteristics.
1 Introduction
Fractional mathematical models have shown the ability to describe the dynamics of some natural phenomena and nonlocal systems that inherit memory properties [1–3]. They are ubiquitous in many areas such as physics, chemistry, biology, control theory, signal and image processing, and economics. For this reason, many of the existing nonlinear PDEs that describe different phenomena have been remodeled in the sense of fractional derivatives, and their chaotic behavior and solutions have been reported in many cases by using developed methods based on wavelets and B-spline collocation ideas [4–9], fractional power (M-)series [10–16], finite difference schemes [17–19], variational iteration methods [20, 21], and (q-)homotopy analysis approaches [22–25].
To mention a few recent works in fractional calculus, a series of interesting results appeared in the literature. For example, the existence and controllability have been investigated for fractional neutral functional (integro)differential equations with nonlocal conditions and with infinite delay in Banach spaces [26–30]. The dynamical structures and the epidemic prophecy have been studied for the novel coronavirus (2019-nCoV) with a nonlocal operator defined in the Caputo sense [31, 32]. The nondifferentiable behavior of heat conduction of the fractal temperature field in homogeneous media was shown in [33]. A fractional epidemiological model describing computer viruses with an arbitrary order derivative having a nonsingular kernel was analyzed in [34]. The dynamics of hepatitis B viral infection with DNA-containing capsids, the liver hepatocytes, and the humoral immune response via fractional differential mathematical models are presented and investigated in [35]. A new immunogenetic tumor model with nonsingular fractional derivative was studied in [36].
Various fractional derivative operators are proposed in the literature. The Riemann–Liouville and Caputo fractional operators are the most common approach of defining fractional derivatives. One common characteristic of these operators is the singularity of their kernels. Although these fractional derivative operators played a vital role in modeling several real-life phenomena, certain phenomena related to material heterogeneities cannot be well modeled in the sense of these fractional derivatives [37]. As a result, general fractional derivatives with nonsingular kernels in terms of the Mittag-Leffler, exponential, trigonometric, Bessel, and Rabotnov fractional-exponential functions were submitted and utilized in modeling many different real-life problems [37–45].
The central focus of almost all the proposed methods was mainly exploring the influence of the time-fractional derivative. However, several studies have revealed that the power-law memory instilled in process and materials could also be in the space coordinate [46, 47]. Motivated by this lack, some analytical methods were recently developed to handle and study mathematical models embedded entirely in a fractional space [48–51]. Continuing in this direction, this research examines the joint influence for the existence of both time and space fractional derivatives in higher-dimensional PDEs. To this end, we merged a new analytical solution representation endowed with multifractional derivative parameters together with the classical power series technique to find the solution of the mathematical models living entirely in a fractional space. Also, we graphically studied the behavior of the obtained solutions and noticed that these solutions converge homotopically, when the Caputo fractional derivatives move from zero to one, to the solution of the integer version of the problem. In some sense, this supports the idea that these fractional derivative parameters act as memory indices.
2 Analytic solution ansatz of higher-dimensional FPDEs
Here we propose an analytic solution ansatz of higher-dimensional partial differential equations entirely living in the fractional space. Then we provide a theoretical frame of the solution ansatz convergence.
Definition 2.1
A trivariate Maclaurin γ̅-series (abbreviated by γ̅-Maclaurin) is a rearrangement for a fractional Cauchy product series in the form
where \(\overline{\pmb{\gamma}}=(\gamma_{1}, \gamma_{2}, \gamma_{3})\in (0,1)^{3}\), \(\{t, x, y \}\) is a set of nonnegative variables, and \(\xi_{\ell_{1},\ell_{2},\ell_{3}}\) are real constants.
We point out here that the γ̅-Maclaurin can be naturally adapted to accommodate the problem under consideration by making the series coefficients as functions in extra variables as follows:
In what follows, we present some convergence theorems related to the γ̅-Maclaurin (2.1). A similar analysis can be adopted to expression (2.2). It should be mentioned here that comparable arguments were presented in a lower-dimensional fractional space in [52].
Lemma 2.2
If there exist\(t_{0}, x_{0}, y_{0}\in\mathbb{R}_{\geq0}\)such that the set\(\{\xi_{\ell_{1},\ell_{2},\ell_{3}} t_{0}^{\ell_{1}\gamma_{1}} x_{0}^{\ell_{2}\gamma_{2}} y_{0}^{\ell_{3}\gamma_{3}}: \ell_{1},\ell_{2},\ell _{3} \in \mathbb{N}^{*} \}\)is bounded, then theγ̅-Maclaurin (2.1) converges absolutely on\(\mathfrak {D}:=[0,t_{0})\times[0,x_{0})\times[0,y_{0})\).
Proof
Let \(t_{0}, x_{0}, y_{0} \in\mathbb{R}_{> 0}\). By assumption we have \(|\xi_{\ell_{1},\ell_{2},\ell_{3}} t_{0}^{\ell_{1}\gamma_{1}} x_{0}^{\ell _{2}\gamma_{2}} y_{0}^{\ell_{3}\gamma_{3}}| \leq M\) for some \(M\in\mathbb {R}_{>0}\) and all \(\ell_{1},\ell_{2},\ell_{3}\in\mathbb{N}^{*}\). Now, for \((t,x,y)\in\mathfrak{D}-\{(0,0,0)\}\), set \(0<\tau_{1}=t t_{0}^{-1}<1\), \(0<\tau_{2}=x x_{0}^{-1}<1\), and \(0<\tau_{3}=y y_{0}^{-1}<1\). Then
Thus, the γ̅-Maclaurin (2.1) converges absolutely on \(\mathfrak{D}\) as desired. Note that if one of \(t_{0}\), \(x_{0}\), or \(y_{0}\) is zero, then a similar argument can be applied with fewer variables. □
Theorem 2.3
Theγ̅-Maclaurin (2.1) converges absolutely on either\(\mathbb{R}_{\geq0}^{3}\)or on\(\mathfrak {D}:=[0,r_{t})\times[0,r_{x})\times[0,r_{y})\)for some\((r_{t}, r_{x}, r_{y}) \in\mathbb{R}^{3}_{\geq0}\). In the latter case the set\(\mathcal {A}(t,x,y):=\{\xi_{\ell_{1},\ell_{2},\ell_{3}} t^{\ell_{1}\gamma_{1}}x^{\ell _{2}\gamma_{2}}y^{\ell_{3}\gamma_{3}}:\ell_{1},\ell_{2},\ell_{3}\in\mathbb{N}^{*}\}\)is unbounded outside\(\mathfrak{D}\).
Proof
Let \(\mathcal{B}:=\{(t,x,y)\in\mathbb{R}_{\geq 0}^{3}:\mathcal{A}(t,x,y)\text{ is bounded}\}\).
-
Case 1.
If \(\mathcal{B}=\mathbb{R}_{\geq0}^{3}\), then the γ̅-Maclaurin converges absolutely on \(\mathbb {R}_{\geq0}^{3}\) by the last lemma.
-
Case 2.
If \(\mathcal{B}\neq\mathbb{R}_{\geq0}^{3}\), then \(\partial\mathcal{B}\) is a nonempty set since \((0,0,0)\in\mathcal {B}\). Let \((r_{t}, r_{x}, r_{y})\in\partial\mathcal{B}\). By the definition of \((r_{t}, r_{x}, r_{y})\), if \((t,x,y)\in\mathfrak{D}\), then there exist \((t_{0},x_{0},y_{0})\in\mathcal{B}\) with \((t,x,y)\in [0,t_{0})\times[0,x_{0})\times[0,y_{0})\). Thus by the last lemma the γ̅-Maclaurin converges absolutely. On the other hand, if \((t,x,y)\in\mathbb{R}_{\geq0}^{3}\) with \((t,x,y)>(r_{t}, r_{x}, r_{y})\), then again by the definition there exist \((t_{1},x_{1},y_{1})\in\mathbb{R}_{\geq0}^{3}-\mathcal{B}\) with \(r_{t}< t_{1}< t\), \(r_{x}< x_{1}< x\), and \(r_{y}< y_{1}< y\). Since \(\mathcal {A}(t_{1},x_{1},y_{1})\) is unbounded, \(\mathcal{A}(t,x,y)\) is unbounded as well.
 □
Definition 2.4
The triple \((r_{t}, r_{x}, r_{y} ) \in\mathbb{R}_{\geq0}^{3}\) in the last theorem is called the triradius of convergence for γ̅-Maclaurin. Otherwise, we say that the triradius of convergence is infinity.
Remark 1
By a suitable change of variable, it is easy to see that the series
with \((t,x,y)\in\mathbb {R}^{3}\) has a triradius of convergence \((r_{t}, r_{x}, r_{y})\) if and only if the γ̅-Maclaurin with \((t,x,y)\in\mathbb {R}_{\geq0}^{3}\) has a triradius of convergence \((r_{t}^{\gamma _{1}^{-1}}, r_{x}^{\gamma_{2}^{-1}}, r_{y}^{\gamma_{3}^{-1}} )\).
Remark 2
It is worth mentioning that the γ̅-Maclaurin is the Cauchy product of fractional power series, after rearrangement, in the domain of absolute convergence. Nevertheless, the γ̅-Maclaurin enables us to add finite elements on each term instead of adding an entire row or column with infinite elements.
where \(\xi_{\ell_{1},\ell_{2},\ell_{3}}=a_{\ell_{1}}b_{\ell_{2}}c_{\ell_{3}}\). These three fractional power series in a single variable will be called the components of γ̅-Maclaurin. Moreover, the γ̅-Maclaurin can be rewritten as the triple sum
Theorem 2.5
If the components ofγ̅-Maclaurin converge absolutely at\(t_{0}, x_{0}, y_{0}>0\), respectively, then theγ̅-Maclaurin\(\sum_{\ell_{1}=0}^{\infty}\sum_{\ell_{2}=0}^{\ell _{1}}\sum_{\ell_{3}=0}^{\ell_{2}} a_{\ell_{1}-\ell_{2}}b_{\ell_{2}-\ell_{3}}c_{\ell _{3}} t^{(\ell_{1}-\ell_{2})\gamma_{1}}x^{(\ell_{2}-\ell_{3})\gamma_{2}}y^{\ell _{3}\gamma_{3}}\)converges absolutely on\(\mathfrak{D}=[0,t_{0})\times [0,x_{0})\times[0,y_{0})\).
Proof
By assumption, \(\sum_{\ell_{1}=0}^{\infty} \vert a_{\ell_{1}}t^{\ell _{1}\gamma _{1}} \vert < L_{1}\), \(\sum_{\ell_{2}=0}^{\infty} \vert b_{\ell_{2}}x^{\ell _{2}\gamma_{2}} \vert < L_{2}\), and \(\sum_{\ell_{3}=0}^{\infty} \vert c_{\ell _{3}}y^{\ell_{3}\gamma_{3}} \vert < L_{3}\) for some \(L_{1}, L_{2}, L_{3} \in\mathbb {R}_{>0}\). For each \(\ell_{1} \in\mathbb{N}\), let \(E_{\ell _{1}}(t_{0},x_{0},y_{0})=\sum_{\ell_{2}=0}^{\ell_{1}}\sum_{\ell_{3}=0}^{\ell_{2}} a_{\ell_{1}-\ell_{2}}b_{\ell_{2}-\ell_{3}}c_{\ell_{3}} t_{0}^{(\ell_{1}-\ell _{2})\gamma _{1}}x_{0}^{(\ell_{2}-\ell_{3})\gamma_{2}}y_{0}^{\ell_{3}\gamma_{3}}\). Then
This shows that the sequence \(\{E_{\ell_{1}}(t_{0},x_{0},y_{0})\}\) is bounded. Therefore the γ̅-Maclaurin converges absolutely on \(\mathfrak{D}\) by Lemma 2.2, as desired. □
Remark 3
It is evident that if the above fractional power series components of the γ̅-Maclaurin have a radius of convergence \(r_{t}\), \(r_{x}\), \(r_{y}\), respectively, then the γ̅-Maclaurin has a triradius of convergence \((r_{t}, r_{x},r_{y})\).
Notation 1
For simplicity, we will alternatively write \(\varGamma (\ell\gamma+1 )\) as \(\varGamma_{\gamma}(\ell)\).
Now, as our goal is to furnish an analytical solution of higher-dimensional FPDEs, we take into account the Caputo fractional derivative, which is defined for an appropriate function as follows:
where \(\gamma\in(0,1)\) is the Caputo fractional derivative order. Accordingly, immediate computations lead to
The following theorem shows the mixed Caputo fractional derivatives of analytic functions in fractional sense of γ̅-Maclaurin [53, 54].
Theorem 2.6
Let\(\omega(t,x,y)\)have aγ̅-Maclaurin on\(\mathfrak{D}=[0,r_{t})\times[0,r_{y}) \times[0,r_{t})\). If\(\mathcal {D}^{i\gamma_{1}}_{t}\mathcal{D}^{j\gamma_{2}}_{x}\mathcal{D}^{k\gamma _{3}}_{y} [\omega(t,x,y) ]\in\mathcal{C} ((0,r_{t})\times(0,r_{x}) \times(0,r_{y}) )\)for\(i, j, k\in\mathbb{N}\). Then
In (2.5), upon substituting \((t,x,y)=(0,0,0)\), we obtain the coefficients in terms of the mixed Caputo fractional derivatives as
which is the fractional version of the classical multivariate Maclaurin coefficients.
3 An application of γ̅-Maclaurin series
In this section, the γ̅-Maclaurin series is merged in the power series technique to furnish analytically the solution of the PDEs endowed with multimemory indices. It is worth recalling here some necessary fractional functions that will be frequently used in the sequel:
-
Mittag-Leffler function: \({ E_{\gamma}(t)= \sum_{\ell =0}^{\infty}\frac{t^{\ell}}{\varGamma_{\gamma}(\ell)}}\),
-
Trigonometric functions: \({ \sin_{\gamma}(t)= \sum_{\ell =0}^{\infty}\frac{(-1)^{\ell} t^{2\ell+1}}{\varGamma_{\gamma}(2\ell +1)}}\), \({ \cos_{\gamma}(t)= \sum_{\ell=0}^{\infty}\frac{(-1)^{\ell} t^{2\ell}}{\varGamma_{\gamma}(2\ell)} }\),
-
Hyperbolic functions: \({ \sinh_{\gamma}(t)= \sum_{\ell =0}^{\infty}\frac{ t^{2\ell+1}}{\varGamma_{\gamma}(2\ell+1)} }\), \({ \cosh_{\gamma}(t)= \sum_{\ell=0}^{\infty}\frac{ t^{2\ell}}{\varGamma _{\gamma}(2\ell)}}\).
Example 1
In our first illustrative example, we consider the following hyperbolic γ̅-wave-like equation:
constrained by the fractional initial conditions
We presume that the solution exists analytically in the form (2.1). Now we substitute the proper formulas from Theorem 2.6 into equations (3.1)–(3.2) and compare the coefficients of identical monomials in both parties to get the following recurrence relation
with initial coefficients
Next, in light of the initial coefficients (3.4), we recursively solve (3.3) to obtain the following general form of the series coefficients:
Finally, we substitute the resulted coefficient (3.5) into the γ̅-Maclaurin series to get
As each of these sums converges absolutely in \([0,\infty)^{3}\) by the ratio test, Remark 2 implies that each sum can be written as a Cauchy product of three series. For example, for the first sum, we have
Therefore the solution (3.6) reduced to the following closed form:
As a particular case, if \(\overline{\pmb{\gamma}}\rightarrow\overline {1}\), then we obtain the solution of the wave-like equation in the integer case:
Figure 1 clarifies the cross-sections of the 10th approximate γ̅-Maclaurin solution (3.6) for several values of \(\overline{\pmb{\gamma}} \in(0,1)^{3}\). Their performance shows that the γ̅-Maclaurin solution depends continuously on the fractional derivative parameters to attain the integer case solution, which in turn reflects some information about memory.
Cross-sections of the 10th approximate solution (3.6)
Example 2
Next, we consider the following γ̅-heat equation:
constrained by the fractional initial condition
Again, we presume that the solution exists analytically in the form (2.1). Now we substitute the proper formulas from Theorem 2.6 into equations (3.9)–(3.10) and compare the coefficients of identical monomials in both parties to get the following recurrence relations for each \(\ell_{i}\geq0\)
with initial coefficient \(\xi_{0,0,2}=1\). Next, we recursively solve (3.11) to obtain the following general form of the series coefficients:
We now substitute the resulting coefficient (3.12) into the γ̅-Maclaurin series to get
As a particular case, if \(\overline{\pmb{\gamma}}\rightarrow\overline {1}\), then we obtain the solution of the heat equation in the integer case:
Figure 2 clarifies the cross-sections of the 10th approximate γ̅-Maclaurin solution (3.13) for several values of \(\overline{\pmb{\gamma}} \in(0,1)^{3}\). Again, their performance shows that the γ̅-Maclaurin solution depends continuously on the fractional derivative parameters to attain the integer case solution, which in turn reflects some information about memory.
Cross-sections of the 10th approximate solution (3.13)
Example 3
Finally, we consider the following γ̅-wave-like equation in 4D:
constrained by the fractional initial conditions
We presume that the solution exists analytically in the form (2.2). We substitute the proper formulas from Theorem 2.6 into equations (3.15)–(3.16) and compare the coefficients of identical monomials in both parties to get the following recurrence-differential equations for each \(\ell_{1}\geq0\):
with initial coefficients
In light of the initial coefficients (3.18), we recursively solve (3.17) to obtain the following general form of the series coefficients
We now substitute the resulting coefficients (3.19) into the γ̅-Maclaurin series to get
As a particular case, if \(\overline{\pmb{\gamma}}\rightarrow\overline {1}\), we obtain the solution of the wave-like equation in the integer 4D case:
Figure 3 clarifies the cross-sections of the 10th approximate γ̅-Maclaurin solution (3.20) for several values of \(\overline{\pmb{\gamma}} \in(0,1)^{3}\). Again, their performance shows that the γ̅-Maclaurin solution depends continuously on the fractional derivative parameters to attain the integer case solution, which in turn reflects some information about memory.
Cross-sections of the 10th approximate solution (3.20)
4 Conclusion
In the current work, we provided an analytical simulation of the mutual impact for the existence of spatial and temporal memory indices in higher-dimensional PDEs in terms of γ̅-Maclaurin series, which is recently developed for the same purpose. Also, we presented a theoretical framework for the convergence of the γ̅-Maclaurin to support our idea. Practically, we employ an amendment of the power series technique to furnish analytically the solution of several well-known physical models with spatial and temporal memory indices together, namely the γ̅-heat and γ̅-wave-like models. The method exhibited a great potentiality in solving such hybrid models, and its performance is validated by comparing the projection of the obtained solutions with the available results in lower fractional spaces. Finally, the graphical analysis shows that the γ̅-Maclaurin solutions are homotopic mappings to attain the integer case solutions, which in turn may reflect some memory characteristics. For this reason, the Caputo fractional derivatives can be considered as memory indices.
References
Du, M., Wang, Z., Hu, H.: Measuring memory with the order of fractional derivative. Sci. Rep. 3, Article ID 3431 (2013). https://doi.org/10.1038/srep03431
Rossikhin, A., Shitikova, M.V.: Application of fractional calculus for dynamic problems of solid mechanics: novel trends and recent results. Appl. Mech. Rev. 63(1), Article ID 010801 (2009). https://doi.org/10.1115/1.4000563
Lundstrom, B.N., Higgs, M.H., Spain, W.J., Fairhall, A.L.: Fractional differentiation by neocortical pyramidal neurons. Nat. Neurosci. 11(11), 1335–1342 (2008). https://doi.org/10.1038/nn.2212
Cattani, C.: A review on harmonic wavelets and their fractional extension. J. Adv. Eng. Comput. 2(4), 224–238 (2018). https://doi.org/10.25073/jaec.201824.225
Kumar, S., Kumar, R., Agarwal, R.P., Samet, B.: A study of fractional Lotka–Volterra population model using Haar wavelet and Adams–Bashforth–Moulton methods. Math. Methods Appl. Sci. 43(8), 5564–5578 (2020). https://doi.org/10.1002/mma.6297
Kumar, S., Kumar, R., Cattani, C., Samet, B.: Chaotic behaviour of fractional predator–prey dynamical system. Chaos Solitons Fractals 135, Article ID 109811 (2020). https://doi.org/10.1016/j.chaos.2020.109811
Kumar, S., Ahmadian, A., Kumar, R., Kumar, D., Singh, J., Baleanu, D., Salimi, M.: An efficient numerical method for fractional SIR epidemic model of infectious disease by using Bernstein wavelets. Mathematics 8(4), Article ID 558 (2020). https://doi.org/10.3390/math8040558
Yuttanan, B., Razzaghi, M.: Legendre wavelets approach for numerical solutions of distributed order fractional differential equations. Appl. Math. Model. 70, 350–364 (2019). https://doi.org/10.1016/j.apm.2019.01.013
Zhu, L., Wang, Y.: Solving fractional partial differential equations by using the second Chebyshev wavelet operational matrix method. Nonlinear Dyn. 89, 1915–1925 (2017). https://doi.org/10.1007/s11071-017-3561-7
Alquran, M., Jaradat, I.: A novel scheme for solving Caputo time-fractional nonlinear equations: theory and application. Nonlinear Dyn. 91(4), 2389–2395 (2018). https://doi.org/10.1007/s11071-017-4019-7
Alquran, M., Jaradat, H.M., Syam, M.I.: Analytical solution of the time-fractional Phi-4 equation by using modified residual power series method. Nonlinear Dyn. 90(4), 2525–2529 (2017). https://doi.org/10.1007/s11071-017-3820-7
Jaradat, I., Al-Dolat, M., Al-Zoubi, K., Alquran, M.: Theory and applications of a more general form for fractional power series expansion. Chaos Solitons Fractals 108, 107–110 (2018). https://doi.org/10.1016/j.chaos.2018.01.039
Jaradat, I., Alquran, M., Al-Dolat, M.: Analytic solution of homogeneous time-invariant fractional IVP. Adv. Differ. Equ. 2018, Article ID 143 (2018). https://doi.org/10.1186/s13662-018-1601-3
Alquran, M., Jaradat, I., Baleanu, D., Syam, M.: The Duffing model endowed with fractional time derivative and multiple pantograph time delays. Rom. J. Phys. 64, Article ID 107 (2019)
Ali, M., Alquran, M., Jaradat, I.: Asymptotic-sequentially solution style for the generalized Caputo time-fractional Newell–Whitehead–Segel system. Adv. Differ. Equ. 2019, Article ID 70 (2019). https://doi.org/10.1186/s13662-019-2021-8
İlhan, E., Kıymaz, İ.O.: A generalization of truncated M-fractional derivative and applications to fractional differential equations. Appl. Math. Nonlinear Sci. 5(1), 171–188 (2020). https://doi.org/10.2478/amns.2020.1.00016
Yokuş, A., Gülbahar, S.: Numerical solutions with linearization techniques of the fractional Harry Dym equation. Appl. Math. Nonlinear Sci. 4(1), 35–42 (2019). https://doi.org/10.2478/AMNS.2019.1.00004
Karatay, I., Bayramoglu, S.R., Sahin, A.: Implicit difference approximation for the time fractional heat equation with the nonlocal condition. Appl. Numer. Math. 61, 1281–1288 (2011). https://doi.org/10.1016/j.apnum.2011.08.007
Meerschaert, M.M., Tadjeran, C.: Finite difference approximations for fractional advection–dispersion flow equations. J. Comput. Appl. Math. 172(1), 65–77 (2004). https://doi.org/10.1016/j.cam.2004.01.033
Wu, G.-C., Baleanu, D.: Variational iteration method for fractional calculus—a universal approach by Laplace transform. Adv. Differ. Equ. 2013, Article ID 18 (2013). https://doi.org/10.1186/1687-1847-2013-18
Wu, G.-C.: A fractional variational iteration method for solving fractional nonlinear differential equations. Comput. Math. Appl. 61(8), 2186–2190 (2011). https://doi.org/10.1016/j.camwa.2010.09.010
Gómez-Aguilar, J., Yépez-MartÃnez, H., Torres-Jiménez, J., Córdova-Fraga, T., Escobar-Jiménez, R.F., Olivares-Peregrino, V.H.: Homotopy perturbation transform method for nonlinear differential equations involving to fractional operator with exponential kernel. Adv. Differ. Equ. 2017, Article ID 68 (2017). https://doi.org/10.1186/s13662-017-1120-7
Bhatter, S., Mathur, A., Kumar, D., Singh, J.: A new analysis of fractional Drinfeld–Sokolov–Wilson model with exponential memory. Physica A 537, Article ID 122578 (2020). https://doi.org/10.1016/j.physa.2019.122578
Kumar, D., Singh, J., Baleanu, D.: A new numerical algorithm for fractional Fitzhugh–Nagumo equation arising in transmission of nerve impulses. Nonlinear Dyn. 91, 307–317 (2018). https://doi.org/10.1007/s11071-017-3870-x
Veeresha, P., Prakasha, D.G., Kumar, S.: A fractional model for propagation of classical optical solitons by using nonsingular derivative. Math. Methods Appl. Sci. (2020). https://doi.org/10.1002/mma.6335
Ravichandran, C., Baleanu, D.: Existence results for fractional neutral functional integro-differential evolution equations with infinite delay in Banach spaces. Adv. Differ. Equ. 2013, Article ID 215 (2013). https://doi.org/10.1186/1687-1847-2013-215
Valliammal, N., Ravichandran, C., Hammouch, Z., Baskonus, H.M.: A new investigation on fractional-ordered neutral differential systems with state-dependent delay. Int. J. Nonlinear Sci. Numer. Simul. 20(7–8), 803–809 (2019). https://doi.org/10.1515/ijnsns-2018-0362
Ravichandran, C., Valliammal, N., Nieto, J.J.: New results on exact controllability of a class of fractional neutral integro-differential systems with state-dependent delay in Banach spaces. J. Franklin Inst. 356(3), 1535–1565 (2019). https://doi.org/10.1016/j.jfranklin.2018.12.001
Valliammal, N., Ravichandran, C., Park, J.H.: On the controllability of fractional neutral integrodifferential delay equations with nonlocal conditions. Math. Methods Appl. Sci. 40(14), 5044–5055 (2017). https://doi.org/10.1002/mma.4369
Zhou, Y., Vijayakumar, V., Ravichandran, C., Murugesu, R.: Controllability results for fractional order neutral functional differential inclusions with infinite delay. Fixed Point Theory 18(2), 773–798 (2017). https://doi.org/10.24193/fpt-ro.2017.2.62
Gao, W., Veeresha, P., Prakasha, D.G., Baskonus, H.M.: Novel dynamic structures of 2019-nCoV with nonlocal operator via powerful computational technique. Biology 9(5), Article ID 107 (2020). https://doi.org/10.3390/biology9050107
Gao, W., Veeresha, P., Baskonus, H.M., Prakasha, D.G., Kumar, P.: A new study of unreported cases of 2019-nCOV epidemic outbreaks. Chaos Solitons Fractals 138, Article ID 109929 (2020). https://doi.org/10.1016/j.chaos.2020.109929
Zhang, Y., Cattani, C., Yang, X.-J.: Local fractional homotopy perturbation method for solving non-homogeneous heat conduction equations in fractal domains. Entropy 17(10), 6753–6764 (2015). https://doi.org/10.3390/e17106753
Singh, J., Kumar, D., Hammouch, Z., Atangana, A.: A fractional epidemiological model for computer viruses pertaining to a new fractional derivative. Appl. Math. Comput. 316, 504–515 (2018). https://doi.org/10.1016/j.amc.2017.08.048
Danane, J., Allali, K., Hammouch, Z.: Mathematical analysis of a fractional differential model of HBV infection with antibody immune response. Chaos Solitons Fractals 136, Article ID 109787 (2020). https://doi.org/10.1016/j.chaos.2020.109787
Ghanbari, B., Kumar, S., Kumar, R.: A study of behaviour for immune and tumor cells in immunogenetic tumour model with non-singular fractional derivative. Chaos Solitons Fractals 133, Article ID 109619 (2020). https://doi.org/10.1016/j.chaos.2020.109619
Caputo, M., Fabrizio, M.: A new definition of fractional derivative without singular kernel. Prog. Fract. Differ. Appl. 1, 73–85 (2015). https://doi.org/10.12785/pfda/010201
Atangana, A., Baleanu, D.: New fractional derivatives with non-local and nonsingular kernel: theory and application to heat transfer model. Therm. Sci. 20(2), 763–769 (2016). https://doi.org/10.2298/TSCI160111018A
Ravichandran, C., Logeswari, K., Jarad, F.: New results on existence in the framework of Atangana–Baleanu derivative for fractional integro-differential equations. Chaos Solitons Fractals 125, 194–200 (2019). https://doi.org/10.1016/j.chaos.2019.05.014
Panda, S.K., Abdeljawad, T., Ravichandran, C.: A complex valued approach to the solutions of Riemann–Liouville integral, Atangana–Baleanu integral operator and non-linear telegraph equation via fixed point method. Chaos Solitons Fractals 130, Article ID 109439 (2020). https://doi.org/10.1016/j.chaos.2019.109439
Owolabi, K.M., Hammouch, Z.: Spatiotemporal patterns in the Belousov–Zhabotinskii reaction systems with Atangana–Baleanu fractional order derivative. Physica A 523, 1072–1090 (2019). https://doi.org/10.1016/j.physa.2019.04.017
Goufo, E.F.D., Kumar, S., Mugisha, S.B.: Similarities in a fifth-order evolution equation with and with no singular kernel. Chaos Solitons Fractals 130, Article ID 109467 (2020). https://doi.org/10.1016/j.chaos.2019.109467
Kumar, S., Ghosh, S., Samet, B., Goufo, E.F.D.: An analysis for heat equations arises in diffusion process using new Yang–Abdel–Aty–Cattani fractional operator. Math. Methods Appl. Sci. 43(9), 6062–6080 (2020). https://doi.org/10.1002/mma.6347
Alshabanat, A., Jleli, M., Kumar, S., Samet, B.: Generalization of Caputo–Fabrizio fractional derivative and applications to electrical circuits. Front. Phys. 8, Article ID 64 (2020). https://doi.org/10.3389/fphy.2020.00064
Yang, X.-J., Abdel-Aty, M., Cattani, C.: A new general fractional-order derivative with Rabotnov fractional-exponential kernel applied to model the anomalous heat transfer. Therm. Sci. 23(3A), 1677–1681 (2019). https://doi.org/10.2298/TSCI180320239Y
Eringen, A.C., Edelen, D.G.: On nonlocal elasticity. Int. J. Eng. Sci. 10(3), 233–248 (1972). https://doi.org/10.1016/0020-7225(72)90039-0
Pandey, V., Näsholm, S.P., Holm, S.: Spatial dispersion of elastic waves in a bar characterized by tempered nonlocal elasticity. Fract. Calc. Appl. Anal. 19(2), 498–515 (2016). https://doi.org/10.1515/fca-2016-0026
Yousef, F., Alquran, M., Jaradat, I., Momani, S., Baleanu, D.: New fractional analytical study of three-dimensional evolution equation equipped with three memory indices. J. Comput. Nonlinear Dyn. 14(11), Article ID 111008 (2019). https://doi.org/10.1115/1.4044585
Jaradat, I., Alquran, M., Yousef, F., Momani, S., Baleanu, D.: On \((2+1)\)-dimensional physical models endowed with decoupled spatial and temporal memory indices. Eur. Phys. J. Plus 134, Article ID 360 (2019). https://doi.org/10.1140/epjp/i2019-12769-8
Jaradat, I., Alquran, M., Katatbeh, Q., Yousef, F., Momani, S., Baleanu, D.: An avant-garde handling of temporal-spatial fractional physical models. Int. J. Nonlinear Sci. Numer. Simul. 21(2), 183–194 (2020). https://doi.org/10.1515/ijnsns-2018-0363
Yousef, F., Alquran, M., Jaradat, I., Momani, S., Baleanu, D.: Ternary-fractional differential transform schema: theory and application. Adv. Differ. Equ. 2019, Article ID 197 (2019). https://doi.org/10.1186/s13662-019-2137-x
Jaradat, I., Alquran, M., Al-Khaled, K.: An analytical study of physical models with inherited temporal and spatial memory. Eur. Phys. J. Plus 133, Article ID 62 (2018). https://doi.org/10.1140/epjp/i2018-12007-1
Alquran, M., Jaradat, I., Baleanu, D., Abdel-Muhsen, R.: An analytical study of \((2+1)\)-dimensional physical models embedded entirely in fractal space. Rom. J. Phys. 64, Article ID 103 (2019)
Alquran, M., Jaradat, I., Abdel-Muhsen, R.: Embedding \((3+1)\)-dimensional diffusion, telegraph, and Burgers’ equations into fractal 2D and 3D spaces: an analytical study. J. King Saud Univ., Sci. 32(1), 349–355 (2020). https://doi.org/10.1016/j.jksus.2018.05.024
Acknowledgements
The authors thank the editor and anonymous reviewers for their valuable suggestions, which substantially improved the quality of the paper.
Availability of data and materials
Not applicable.
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
The authors declare that this study was accomplished in collaboration with the same responsibility. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Competing interests
The authors declare that there are no conflicts of interest regarding the publication of this article.
Consent for publication
All authors read and approved the final version of the manuscript.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Jaradat, I., Alquran, M., Abdel-Muhsen, R. et al. Higher-dimensional physical models with multimemory indices: analytic solution and convergence analysis. Adv Differ Equ 2020, 364 (2020). https://doi.org/10.1186/s13662-020-02822-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s13662-020-02822-7
MSC
- 26A33
- 41A58
- 35R11
- 35C10
Keywords
- Memory index
- Fractional PDEs
- Analytic solution