Abstract
The paper aims to present an efficient numerical scheme to quantify the uncertainty in the solution of stochastic fractional integro-differential equations. The numerical scheme presented here is based on Legendre wavelets combined with block pulse functions using their deterministic and stochastic operational matrix of integration. The operational matrices are utilized to convert the stochastic fractional integro-differential equation to a linear system of algebraic equation. Finally, the accuracy and efficiency of the proposed scheme are investigated through numerical experiments.
Supported by University Grants Commission, New Delhi-110002, India.
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Keywords
- Legendre polynomial
- Legendre wavelets
- Stochastic operational matrix
- It\(\hat{o}\) integral
- Integro-differential equations
1 Introduction
A stochastic fractional integro-differential equation (SFIDE), where order of derivative is non integer, is a generalization of the fractional Folkker-Plank equation which describes the random walk of a particle [2]. This model has the following form
where \(D^{\alpha },\) \( 0< \alpha < 1,\) denotes the Caputo fractional derivative, \(W(s),s \in [0,T)\) is the standard Wiener process and the integral with respect to it is the It\(\hat{o}\) integral. Presence of the It\(\hat{o}\) integral in Equation (1) causes randomness in the solution and hence it becomes non deterministic. In this paper, we develop a novel approach to quantify this uncertain behavior in the numerical solution.
In recent decade, the need to obtain the numerical solution of SFIDE has increased significantly. However, in literature, only a handful of papers are available that actually discuss about the numerical solution of SFIDE. In [8], Maleknejad et al. provided an operational matrix method based on block pulse functions to solve stochastic Volterra integral equations. In [12], Taheri et al. formulated the spectral collocation method based on shifted Legendre polynomials to solve SFIDE. In [10], Mirzaee and Samadyar constructed an efficient scheme to solve SFIDE based on Bernstein polynomials. In [11], Mirzaee and Samadyar provide a meshless discrete collocation method based on radial basis functions to solve SFIDE.
In this paper, a new scheme is derived based on Legendre wavelet collocation method and block pulse function involving the operational matrix for solving SFIDE (1). In Sect. 2, we give basic definition of fractional calculus and construction of Legendre wavelet based on Multi-resolution analysis. Then in Sect. 3, operational matrix of fractional order integration and integration operational matrix are derived. The proposed scheme for the SFIDE is discussed in Sect. 4, while Sect. 5 provides numerical experiments performed to showcase the effectiveness of the approach. In Sect. 6, we present various applications of SFIDE. Finally, Sect. 7 gives the brief conclusion.
2 Preliminaries
In this section, we discuss the mathematical preliminaries of fractional calculus and construction of wavelet which are required for subsequent development.
Definition 1
[5] The left Riemann-Liouville fractional integral of order \(\alpha \ge 0\) of a function \(f(t),~t\in (a,b)\) is defined as follows
Similar to integer order integration, the left Riemann-Liouville fractional integral operator is a linear operator
where \(\lambda \) and \(\mu \) are constants.
Definition 2
[5] The left Caputo derivative with order \(\alpha > 0\) of the given function \(f(t),~t\in (a,b)\) is defined as
where m is a positive integer satisfying \(m-1 < \alpha \le m.\)
2.1 Multi-resolution Analysis (MRA)
An MRA is an increasing family of closed subspace \(V^{j} \subset L^{2}(\mathbb {R})\) which satisfies the following axioms [9] :
-
1.
\(V^{j} \subset V^{j+1}\)
-
2.
\(\overline{\cup _{j \in \mathbb {Z}}V^{j}} = L^{2}(\mathbb {R})\)
-
3.
\(\{\phi (x-k) : k \in \mathbb {Z}\}\) is an orthonormal basis of \(V^{0}\)
-
4.
\(f(\cdot ) \in V^{j}\) if and only if \(f(2(\cdot )) \in V^{j+1}\) for all \(j \in \mathbb {Z}\).
For given nested sequence subspace \(V^{j}\), define the space \(W^{j}\) as the orthogonal complement of \(V^{j}\) in \(V^{j+1}\), i.e., \(V^{j} \perp W^{j}\) and
applying recursively, we get
Now, based on the above analysis, to construct a wavelet define a space \(V_{M}^{J}\) of piecewise polynomial functions as follows :
The space \(V_{M}^{J}\) has dimension \(2^{(J-1)}M\) and
Next, consider the \(2^{(J-1)}M\)-dimensional space \(W_{M}^{J}\) which is an orthogonal complement of \(V_{M}^{J}\) in \(V_{M}^{J+1}\), i.e.,
Inductively, one can obtain
Unlike Haar, the element of the space \(W_{M}^{J}\) do not have a general form. To construct the elements of \(W_{M}^{J}\) one can refer [1].
Further, if
then we define the projection operator \(P_{V_{M}^{J}} : L^{2}[0,1] \rightarrow V_{M}^{J}\) as
where \(c_{k,m}^{J} = \int _{\frac{(k-1)}{2^{(J-1)}}}^{\frac{k}{2^{(J-1)}}}f(x)\phi _{k,m}^{J}(x)dx.\) Set
where \(\psi _{k,m}^{J}(x) = 2^{\frac{(J-1)}{2}}\psi _{m}(2^{(J-1)}x-k+1)\). The support of \(\psi _{k,m}^{J}\) is \([\frac{k-1}{2^{J-1}},\frac{k}{2^{J-1}})\) and \(\psi _{m}(x)\) satisfies the following property (vanishing moment property)
Now, using \(V_{M}^{J}\), we introduced the subspace \(V_{M}^{J,2}\) of \(L^{2}([0,)\times [0,1))\) defined by
Moreover,
Then, define the projection operator \(P_{V_{M}^{J,2}}:L^{2}([0,1]\times [0,1]) \rightarrow V_{M}^{J,2}\) as
where \(c_{k,m,k',m'}^{J} = \int _{\frac{(k-1)}{2^{(J-1)}}}^{\frac{k}{2^{J-1}}}\int _{\frac{(k'-1)}{2^{(J-1)}}}^{\frac{k'}{2^{J-1}}}f(s,t)\phi _{k,m,k',m'}^{J}(s,t)dsdt\).
Next, introduce the space \(W_{M}^{J,2}\) which is defined by
and
where
and \(\psi _{m,m'}(s,t)\) satisfies the following property
The subspace \(W_{M}^{J,2}\) is orthogonal complement of \(V_{M}^{J,2}\) in \(V_{M}^{J+1,2}\). Therefore, one can write
and hence
Now, if we choose \(\phi _{k,m}^{J}\) as in [13], i.e., for \(m = 0,1,2,\cdots ,M-1\), and \(\hat{k} = 2k-1\) with \(k=1,2,\cdots ,2^{(J-1)}\)
where \(P_{m}(t)\) is a Legendre polynomials of order m are defined in the interval \([-1,1]\) and given by the following recurrence formulas
The wavelet constructed above using Legendre polynomials are called as Legendre wavelet [7].
2.2 Function Approximation
A function f(t) defined over \(L^{2}[0,1)\) can be expanded with Legendre scaling functions \(\phi _{k,m}^{J}(t)\) as
where \(c_{k,m}^{J} = \int _{0}^{1}f(t)\phi _{k,m}^{J}(t)dt\) and \(J\rightarrow \infty \). If the infinite series in (14) is truncated, then (14) can be written as
where C and \(\varPhi (t)\) are \(2^{J-1}M\times 1\) matrices given by
In similar way, a bivariate function \(f(s,t) \in L^{2}[[0,1)\times [0,1)]\) can be expanded with Legendre wavelets as
where
3 Legendre Wavelet Matrix and Block Pulse Operational Matrix
Let the collocation points be
We denote the Legendre wavelet matrix as \(\phi _{2^{J-1}M\times 2^{J-1}M}\) and define it as the combination of \(\phi _{k,m}^{J}(t_{i})\) at the collocation points \((t_{i})\) as
3.1 Legendre Wavelet Operational Matrix of Fractional Order Integration
If f(t) is expanded as in Eq. (14), then the Riemann-Liouville fractional order integration is given by
The \(2^{J-1}M-\)set of block pulse functions (BPFs) are also defined as
where \(i = 1,2,\dots ,2^{J-1}M\). The function \(b_{i}(t)\) has the following disjoint and orthogonal properties
-
\(b_{i}(t)b_{j}(t) = \delta _{ij}b_{i}(t)\),
-
\(\int _{0}^{1}b_{i}(t)b_{j}(t) = \frac{\delta _{ij}}{2^{J-1}M}\),
where \(\delta _{ij}\) is the Kronecker delta. The Legendre wavelet can be expanded into \(2^{J-1}M\) - term block pulse function as
where \(\mathbf{b} _{2^{J-1}M}(t) = [b_{1}(t),\dots ,b_{2^{J-1}M}(t)]^{T}.\) The block pulse operational matrix of fractional-order integration \(G^{\alpha }\) is given in [6] as follows
where
with \(\xi _{i} = (i+1)^{\alpha +1}-2i^{\alpha +1}+(i-1)^{\alpha +1}\).
Let
where the matrix \(P_{2^{J-1}M\times 2^{J-1}M}^{\alpha }\) is called the Legendre wavelet operational matrix of fractional order integration. Using Eqs. (18) and (19) in (20), we get
3.2 Deterministic Integration Operational Matrix
Let \(m = 2^{J-1}M\) and compute \(\int _{0}^{t}b_{i}(s)ds\) as follows
We approximate \(t-\frac{i-1}{m}\), for \(\frac{i-1}{m} \le t < \frac{i}{m},\) by \(\frac{1}{2m}\) and express \(\int _{0}^{t}b_{i}(s)ds\) in terms of BPFs as follows
where \(\frac{1}{2m}\) is the ith component of vector. Therefore
where the operational matrix of integration is given by
3.3 Stochastic Integration Operational Matrix
The It\(\hat{o}\) integral of each single BPFs \(b_{i}(t)\) can be computed as follows
We can approximate \(W(t)-W(\frac{i-1}{m})\), for \(\frac{i-1}{m} \le t < \frac{i}{m}\), by \(W(\frac{i-0.5}{m})-W(\frac{i-1}{m})\) and express \(\int _{0}^{t}b_{i}(s)dW(s)\), in terms of BPFs as follows
where \(W(\frac{i-0.5}{m})-W(\frac{i-1}{m})\) is the ith component of vector. Therefore, we obtain the following expression (for details, see [8])
where stochastic operational matrix of integration is given by
4 Description of Numerical Method
Here we present the wavelet collocation method based on the Legendre wavelets for solving SFIDE (1). We use the relation between the fractional derivative and integral to obtain the solution u(t) derived as follows
-
Let \(D^{\alpha }u(t) \approx C^{T}\varPhi (t)\), this implies that
$$\begin{aligned} u(t) \approx {C^{T}} {_{0}I^{\alpha }}\varPhi (t)+u_{0}. \end{aligned}$$(27) -
Let \(k_{1}(s,t) \in L^{2}([0,1)\times [0,1))\). It can be expanded with respect to Legendre wavelet as
$$\begin{aligned} k_{1}(s,t) \approx \varPhi ^{T}(s)K_{1}\varPhi (t) = \varPhi ^{T}(t)K_{1}^{T}\varPhi (s), \end{aligned}$$(28)where \(K_{1} = (k_{1})_{ij},~i=1,2,\dots ,m,~j=1,2,\dots ,m\) is the \(m\times m\) Legendre wavelets coefficient matrix with
$$\begin{aligned} (k_{1})_{ij} = \int _{0}^{1}\int _{0}^{1}k_{1}(s,t)\phi _{i}(s)\phi _{j}(t)dsdt. \end{aligned}$$Similarly
$$\begin{aligned} k_{2}(s,t) \approx \varPhi ^{T}(s)K_{2}\varPhi (t) = \varPhi ^{T}(t)K_{2}^{T}\varPhi (s), \end{aligned}$$(29)where \(K_{2} = (k_{2})_{ij},~i=1,2,\dots ,m,~j=1,2,\dots ,m\) is the \(m\times m\) Legendre wavelets coefficient matrix with
$$\begin{aligned} (k_{2})_{ij} = \int _{0}^{1}\int _{0}^{1}k_{2}(s,t)\phi _{i}(s)\phi _{j}(t)dsdt. \end{aligned}$$
Substituting the above approximation in (1), we get
Let \(P^{\alpha }\phi = Q_{1},~\phi ^{T}K_{1} = Q_{2},~\phi ^{T}K_{2} = Q_{3}\), \(Q_{2}^{i}\) be the ith row of constant matrix \(Q_{2}\), \(Q_{3}^{i}\) be the ith row of constant matrix \(Q_{3}\), \(R^{i}\) be the ith row of the integration operational matrix P and \(R_{s}^{i}\) be the ith row of the stochastic operational matrix \(P_{s}\). We have
Then
So, by setting
and
where \(t_{i}\) are the collocation points. We have
which is a linear system of equations that gives the Legendre wavelets coefficient.
5 Numerical Experiments
To illustrate the proposed method discussed in Sect. 4, we consider the following examples.
Example 51
Consider the SFIDE (1) with \(f(t) = \frac{t^{2}}{2}+\frac{\varGamma (2)}{\varGamma (2-\alpha )}t^{1-\alpha },~k_{1}(s,t) = 1,~k_{2}(s,t) = 0,\) and \(u_{0} = 0\). For \(\alpha = 0\), \(u(t) = -(2+t)+2e^{t}\) is exact solution of (1).
Example 52
Consider the SFIDE (1) with \(f(t) = -\frac{t^{5}e^{t}}{5}+\frac{6t^{2.25}}{\varGamma (3.25)},~k_{1}(s,t) = e^{t}s,~k_{2}(s,t) = 0,\) and \(u_{0} = 0\). For \(\alpha = 0.75\), \(u(t) = t^{3}\) is exact solution of (1).
Example 53
Consider the SFIDE (1) with \(f(t) = 0,~k_{1}(s,t) = s^{2},~k_{2}(s,t) = s^{3},\) and \(u_{0} = 1\). For \(\alpha = 0\), \(u(t) = e^{\frac{t^{3}}{3}+\int _{0}^{t}s^{3}dW(s)}\) is exact solution of (1).
Let \(u_{num}(t_{i},l)\) denotes the approximate solution of \(l^{th}\) simulation at \(t_{i}\) and \(u_{exact}(t_{i},l)\) denotes the exact solution of \(l^{th}\) simulation at \(t_{i}\). The efficiency of the proposed method, for Examples 51, 52 and 53 are highlighted in Tables 1, 2 and 3, respectively, which showcase the values of maximum absolute error and root mean square (RMS) error that are defined as
respectively, where N is total number of simulation and \(\mathbb {E}\) is mathematical expectation. For deterministic function \(\mathbb {E}\Vert .\Vert _{\infty } = \Vert .\Vert _{\infty }\) and \(\mathbb {E}\Vert .\Vert _{2,m} = \Vert .\Vert _{2,m}\). Table 1 shows the maximum absolute errors obtained for Example 51 via the proposed method discussed in Sect. 4 for \(\alpha = 0\) and different values of J, M and m. Table 2 shows the comparison of our method with Gaussian radial basis function (GA RBF) and thin plate splines radial basis function (TBS RBF) in terms of the absolute maximum error and RMS-error obtained for Example 52 with \(\alpha =0.75\) and different values of m. These methods (GA RBF and TBS RBF [11]) need smaller value of shape parameter for higher accuracy which increases the condition number of coefficient matrix and as a result the methods become unstable. However, the method proposed in Sect. 4 has no such behavior. Finally, Table 3 shows the calculation of the mean and standard deviation which are denoted by \(\mathbb {E}\Vert e\Vert _{\infty }\) and \(S_{e}\), respectively, of the maximum absolute error for Example 53 with \(\alpha = 0\) and different number of simulation trajectories (N). For different values of N, the upper and lower limit of \(95\%\) confidence interval (C.I.) are also listed in Table 3. In Fig. 1, we plot the mean approximate solution and mean exact solution of Example 53 along with \(95\%\) confidence interval region for \(\alpha = 0\) and \(m =32\) with different values of N.
6 Applications
In this section, we present some special cases of the proposed model and their applications in real life examples. SFIDE (1) have many practical applications in scientific field such as physics, finance and biology etc. When \(\alpha =0\), \(f(t)=0\), \(k_1(s,t)=\mu \) and \(k_2(s,t)=\sigma \), the proposed model reduces in the following form
or,
The above stochastic differential equation is called geometric Brownian motion model which is used for modeling the stock prices in the finance [3]. Also in physics, (35) is the Langevin equation with multiplicative noise [4]. The Langevin equation is very important tool in physics for describing many physical process. Suppose u(t) is any physical process described by the (35) then we always get probability distribution \(p(u,t|u_{0})\) corresponding to (35) which satisfies the Fokker-Plank equation.
7 Conclusion
In this paper, we develop a wavelet collocation method based on the Legendre wavelets to solve SFIDE (1). For this purpose, we compute the deterministic and stochastic operational matrices based on block pulse function. The SFIDE (1) is then converted to a system of linear equations by employing the collocation method and making use of operational matrices. The solution of SFIDE is obtained using our proposed method. In Sect. 5, we solve several examples to indicate the accuracy and efficiency of the proposed method as discussed in Sect. 4.
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Singh, A.K., Mehra, M. (2020). Uncertainty Quantification in Fractional Stochastic Integro-Differential Equations Using Legendre Wavelet Collocation Method. In: Krzhizhanovskaya, V., et al. Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science(), vol 12138. Springer, Cham. https://doi.org/10.1007/978-3-030-50417-5_5
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