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Higher Order Approximation for Stochastic Space Fractional Wave Equation Forced by an Additive Space-Time Gaussian Noise

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Abstract

The infinitesimal generator (fractional Laplacian) of a process obtained by subordinating a killed Brownian motion catches the power-law attenuation of wave propagation. This paper studies the numerical schemes for the stochastic wave equation with fractional Laplacian as the space operator, the noise term of which is an infinite dimensional Brownian motion or fractional Brownian motion (fBm). Firstly, we establish the regularity of the mild solution of the stochastic fractional wave equation. Then a spectral Galerkin method is used for the approximation in space, and the space convergence rate is improved by postprocessing the infinite dimensional Gaussian noise. In the temporal direction, when the time derivative of the mild solution is bounded in the sense of mean-squared \(L^p\)-norm, we propose a modified stochastic trigonometric method, getting a higher strong convergence rate than the existing results, i.e., the time convergence rate is bigger than 1. Particularly, for time discretization, the provided method can achieve an order of 2 at the expenses of requiring some extra regularity to the mild solution. The theoretical error estimates are confirmed by numerical experiments.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 12071195 and the AI and Big Data Funds under Grant No. 2019620005000775.

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Correspondence to Weihua Deng.

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Appendices

A Definitions of the cosine and sine Operators

In term of the eigenpairs \(\left\{ \left( \lambda _i,\phi _i\right) \right\} _{i=1}^\infty \), the cosine and sine operators can be expressed as

$$\begin{aligned} \sin \left( A^\alpha t\right) u(t)= & {} \sum ^\infty _{i=1}\sin \left( \lambda _i^\alpha t\right) \left\langle u(t),\phi _{i}(x)\right\rangle \phi _{i}(x)\\= & {} \sum ^\infty _{i=1}\sum ^\infty _{j=1}(-1)^{j-1}\frac{\left( \lambda _i^\alpha t\right) ^{2j-1}}{(2j-1)!}\left\langle u(t),\phi _{i}(x)\right\rangle \phi _{i}(x) \end{aligned}$$

and

$$\begin{aligned} \cos \left( A^\alpha t\right) u(t)= & {} \sum ^\infty _{i=1}\cos \left( \lambda _i^\alpha t\right) \left\langle u(t),\phi _{i}(x)\right\rangle \phi _{i}(x)\\= & {} \sum ^\infty _{i=1}\sum ^\infty _{j=0}(-1)^{j}\frac{\left( \lambda _i^\alpha t\right) ^{2j}}{(2j)!}\left\langle u(t),\phi _{i}(x)\right\rangle \phi _{i}(x). \end{aligned}$$

B Simulation of Stochastic Integral for fBm

Suppose \(0\le t_1\le \dots \le t_{m}\le \dots \le t_{M}=T\) \((m=1,2,\dots , M-1)\) and the fixed sizes of the mesh \(\tau =t_{m+1}-t_{m}\). Let’s consider the following vector

$$\begin{aligned} Z=\left( \int ^{t_{1}}_{0}s\mathrm {d}\beta _H(s),\int ^{t_{2}}_{t_{1}}(s-t_1)\mathrm {d}\beta _H(s),\dots , \int ^{t_{M}}_{t_{M-1}}(s-t_{M-1})\mathrm {d}\beta _H(s)\right) . \end{aligned}$$

The stochastic integral \(\int ^{t_{m+1}}_{t_{m}}(s-t_m)\mathrm {d}\beta _H(s)\) is a Gaussian process with mean 0. The Cholesky method can be applied to stationary and non-stationary Gaussian processes. Thus, we use the Ckolesky method to simulate (5.10). The probability distribution of the vector Z is normal with mean 0 and the covariance matrix \(\varSigma \). Let \(\varSigma _{i,j}\) be the element of row i, column j of matrix \(\varSigma \). Then

$$\begin{aligned} \varSigma _{i,j}= & {} \mathrm {E}\left[ \int ^{t_{j+1}}_{t_j}(s-t_j)\mathrm {d}\beta _H(s)\int ^{t_{k+1}}_{t_{k}}(t-t_k)\mathrm {d}\beta _H(t)\right] . \end{aligned}$$

By using Lemma 2.2, for \(j>k\), we have

$$\begin{aligned}&\mathrm {E}&\left[ \int ^{t_{j+1}}_{t_j}(s-t_j)\mathrm {d}B_H(s)\int ^{t_{k+1}}_{t_{k}}(t-t_k)\mathrm {d}B_H(t)\right] \\= & {} H(2H-2)\int ^{t_{j+1}}_{t_j}\int ^{t_{k+1}}_{t_{k}}(s-t_j)(t-t_k)(s-t)^{2H-2}\mathrm {d}t\mathrm {d}s\\= & {} -\frac{\tau ^2}{2}(t_{j+1}-t_{k+1})^{2H}+\frac{\tau }{2(2H+1)}\left( (t_{j+1}-t_{k})^{2H+1}-(t_{j}-t_{k+1})^{2H+1}\right) \\&-\frac{1}{2(2H+1)(2H+2)}\left( (t_{j+1}-t_{k})^{2H+2}-2(t_{j}-t_{k})^{2H+2}+(t_{j}-t_{k+1})^{2H+2}\right) \\= & {} -\frac{\tau ^{2+2H}}{2}(j-k)^{2H}+\frac{\tau ^{2+2H}}{2(2H+1)}\left( (j+1-k)^{2H+1}-(j-k-1)^{2H+1}\right) \\&-\frac{\tau ^{2+2H}}{2(2H+1)(2H+2)}\left( (j+1-k)^{2H+2}-2(j-k)^{2H+2}+(j-k-1)^{2H+2}\right) . \end{aligned}$$

When \(j=k\),

$$\begin{aligned} \mathrm {E}\left[ \int ^{t_{j+1}}_{t_j}(s-t_j)\mathrm {d}B_H(s)\int ^{t_{j+1}}_{t_{j}}(t-t_j)\mathrm {d}B_H(t)\right] =\frac{\tau ^{2H+2}}{2H+2}. \end{aligned}$$

When \(\varSigma \) is a symmetric positive matrix, the covariance matrix \(\varSigma \) can be written as \(L(M)L(M)'\), where the matrix L(M) is lower triangular matrix and the matrix \(L(M)'\) is the transpose of L(M). Let \(V=(V_1,V_2,\dots ,V_M)\). The elements of the vector V are a sequence of independent and identically distributed standard normal random variables. Since \(Z=L(M)V\), then Z can be simulated. Let \(l_{i,j}\) be the element of row i, column j of matrix L(M). That is,

$$\begin{aligned} \varSigma _{i,j}=\sum ^j_{k=1}l_{i,k}l_{j,k}, \quad j\le i. \end{aligned}$$

As \(i=j=1\), we have \(l^2_{1,1}=\varSigma _{1,1}\). The \(l_{i,j}\) satisfies

$$\begin{aligned} l_{i+1,1}= & {} \frac{\varSigma _{i+1,1}}{l_{1,1}},\\ l^2_{i+1,i+1}= & {} \varSigma _{i+1,i+1}-\sum ^{i}_{k=1}l^2_{i+1,k},\\ l_{i+1,j}= & {} \frac{1}{l_{j,j}}\left( \varSigma _{i+1,j}-\sum ^{j-1}_{k=1}l_{i+1,k}l_{j,k}\right) ,\quad 1<j\le i. \end{aligned}$$

C Proof of Theorem 5.2

Proof

First, combining (4.7), (5.8), (5.11), the assumptions, and Corollary 2 leads to

$$\begin{aligned}&\left\| u^N(t_{m+1})-u^N_{m+1}\right\| _{L^2(D,U)} \lesssim \left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\right. \nonumber \\&\left. \sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \left[ f_N\left( u^N(s)\right) -f_N\left( u^N(t_j)\right) -\int ^s_{t_j}f'_N\left( u^N(t_j)\right) v^N(t_j)\mathrm {d}r\right] \mathrm {d}s\right\| _{L^2(D,U)}\nonumber \\&~~~+\left\| \sum ^{m}_{j=1}A^{-\alpha }\frac{ \tau \cos \left( A^{\frac{\alpha }{2}}(t_{m+1}-t_{j+1})\right) -\int ^{t_{j+1}}_{t_j}\cos \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \mathrm {d}s}{\tau }\right. \nonumber \\&~~~\times \left. \left[ \tau f'_N\left( u^N(t_j)\right) v^N(t_j)-f_N\left( u^N_j\right) +f_N\left( u^N_{j-1}\right) \right] \right\| _{L^2(D,U)}\nonumber \\&~~~+\left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \left[ f_N\left( u^N(t_j)\right) -f_N\left( u^N_j\right) \right] \mathrm {d}s\right\| _{L^2(D,U)}\nonumber \\&~~~+\left\| \int ^{t_{1}}_{0}A^{-\frac{\alpha }{2}}\right. \nonumber \\&\left. \sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \left[ f_N\left( u^N(s)\right) -f_N\left( u^N_0\right) \right] \mathrm {d}s\right\| _{L^2(D,U)}+\tau ^{\min \left\{ \frac{\gamma }{\alpha },2\right\} }\left( \sum ^{N_1}_{i=0}\lambda _i ^{\min \{\gamma -\alpha ,\alpha \}-2\rho }\right) ^{\frac{1}{2}}\nonumber \\&\lesssim J_1+J_2+\sum ^{m}_{j=0}\tau \left\| u^N(t_j)-u^N_j\right\| _{L^2(D,U)}+\tau ^2+\tau ^{\min \left\{ \frac{\gamma }{\alpha },2\right\} }\left( \sum ^{N_1}_{i=0}\lambda _i ^{\min \{\gamma -\alpha ,\alpha \}-2\rho }\right) ^{\frac{1}{2}}. \end{aligned}$$
(C.1)

When \(\frac{1}{2}<H<1\), let \(\theta =\min \left\{ \frac{\gamma -\alpha }{\alpha },1\right\} \). We have

$$\begin{aligned} J_1\lesssim & {} \left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \right. \\&\left. \times \left[ \int ^s_{t_j}f'_N\left( u^N(r)\right) v^N(r)\mathrm {d}r-\int ^s_{t_j}f'_N\left( u^N(t_j)\right) v^N(t_j)\mathrm {d}r\right] \mathrm {d}s\right\| _{L^2(D,U)}\\\lesssim & {} \left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \right. \\&\left. \times \int ^s_{t_j}\left( f'_N\left( u^N(r)\right) -f'_N\left( u^N(t_j)\right) \right) v^N(r)\mathrm {d}r\mathrm {d}s\right\| _{L^2(D,U)}\\&+\left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \right. \\&\left. \times \int ^s_{t_j}f'_N\left( u^N(t_j)\right) \left( \cos \left( A^{\frac{\alpha }{2}}(r-t_j)\right) -I\right) v^N(t_j)\mathrm {d}r\mathrm {d}s\right\| _{L^2(D,U)}\\&+\left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \right. \\&\left. \times \int ^s_{t_j}f'_N\left( u^N(t_j)\right) \left( v^N(r)-\cos \left( A^{\frac{\alpha }{2}}(r-t_j)\right) v^N(t_j)\right) \mathrm {d}r\mathrm {d}s\right\| _{L^2(D,U)}\\\lesssim & {} \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}\int ^s_{t_j}\left\| \left( u^N(r)-u^N(t_j)\right) \right. \\&\left. \times v^N(r)\right\| _{L^2(D,U)}\mathrm {d}r\mathrm {d}s+\sum ^{m}_{j=1}\tau ^\theta \int ^{t_{j+1}}_{t_j}\int ^s_{t_j}\left\| A^{\frac{\theta \alpha }{2}}v^N(t_j)\right\| _{L^2(D,U)}\mathrm {d}r\mathrm {d}s+II\\\lesssim & {} \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}\int ^s_{t_j}\int ^r_{t_j}\left\| v^N(t)v^N(r)\right\| _{L^2(D,U)}\mathrm {d}t\mathrm {d}r\mathrm {d}\\&+\sum ^{m}_{j=1}\tau ^{2+\theta }\left\| A^{\frac{\theta \alpha }{2}}v^N(t_j)\right\| _{L^2(D,U)}+II\\\lesssim & {} \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}\int ^s_{t_j}\int ^r_{t_j}\left\| \left| v^N(t)\right| ^2+\left| v^N(r)\right| ^2\right\| _{L^2(D,U)}\mathrm {d}t\mathrm {d}r\mathrm {d}s\\&+\sum ^{m}_{j=1}\tau ^{2+\theta }\left\| A^{\frac{\theta \alpha }{2}}v^N(t_j)\right\| _{L^2(D,U)}+II\\\lesssim & {} \tau ^2\left( 1+\left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) \\&+\sum ^{m}_{j=1}\tau ^{2+\theta }\left\| A^{\frac{\theta \alpha }{2}}v^N(t_j)\right\| _{L^2(D,U)}+II. \end{aligned}$$

The condition \(\gamma >\alpha \) implies that \(\rho >\frac{d}{4}\). Then combining the fact that \(\{\beta ^{i}_{H}(t)\}_{i\in {\mathbb {N}}}\) are mutually independent, Lemma 2.2, and (4.10), we have

$$\begin{aligned} II= & {} \left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \int ^s_{t_j}f'_N\left( u^N(t_j)\right) \right. \\&\times \left. \left[ -A^{\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(r-t_j)\right) u^N(t_j)+\int ^{r}_{t_j}\cos \left( A^{\frac{\alpha }{2}}(r-y)\right) f_N\left( u^N(y)\right) \mathrm {d}y\right. \right. \\&+\left. \left. \int ^{r}_{t_j}\sum ^{N_1}_{i=1}\cos \left( \lambda _i^{\frac{\alpha }{2}}(r-y)\right) \sigma _i\phi _i(x)\mathrm {d}\beta _H^i(y)\right] \mathrm {d}r\mathrm {d}s\right\| _{L^2(D,U)}\\\lesssim & {} \tau ^{2+\theta }\sum ^{m}_{j=1}\left\| A^{\frac{\alpha (\theta +1)}{2}}u^N(t_j)\right\| _{L^2(D,U)}+\tau ^2\left( T^H+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right) \\&+\left( \sum ^{N_1}_{i=1}\lambda _i^{-2\rho }\mathrm {E}\left[ \left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \int ^s_{t_j}f'_N\left( u^N(t_j)\right) \right. \right. \right. \\&\times \left. \left. \left. \int ^{r}_{t_j}\cos \left( \lambda _i^{\frac{\alpha }{2}}(r-y)\right) \phi _i(x)\mathrm {d}\beta _H^i(y)\mathrm {d}r\mathrm {d}s\right\| ^2\right] \right) ^{\frac{1}{2}}\\\lesssim & {} \left( \sum ^{N_1}_{i=1}\lambda _i^{-2\rho }\mathrm {E}\left[ \int _D\sum ^{m}_{j=1}\sum ^{m}_{k=1}\int ^{t_{j+1}}_{t_j}\int ^s_{t_j}\int ^{t_{k+1}}_{k_j}\int ^t_{k_j}\int ^{r}_{t_j}\int ^{r_1}_{t_k}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \right. \right. \\&\times \left. \left. f'_N\left( u^N(t_j)\right) \cos \left( \lambda _i^{\frac{\alpha }{2}}(r-y)\right) \phi _i(x)A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-t)\right) f'_N\left( u^N(t_k)\right) \right. \right. \\&\times \left. \left. \cos \left( \lambda _i^{\frac{\alpha }{2}}(r_1-y_1)\right) \phi _i(x)|y-y_1|^{2H-2}\mathrm {d}y\mathrm {d}y_1\mathrm {d}r\mathrm {d}s\mathrm {d}r_1\mathrm {d}t\mathrm {d}x\right] \right) ^{\frac{1}{2}}\\&+\tau ^{2+\theta }\sum ^{m}_{j=1}\left\| A^{\frac{\alpha (\theta +1)}{2}}u^N(t_j)\right\| _{L^2(D,U)}\\\lesssim & {} \left( \tau ^4\sum ^{m}_{j=1}\sum ^{m}_{k=1}\mathrm {E}\left[ \left\| f'_N\left( u^N(t_j)\right) \phi _i(x)\right\| \left\| f'_N\left( u^N(t_k)\right) \phi _i(x)\right\| \right] \right. \\&\times \left. \int ^{t_{j+1}}_{t_j}\int ^{t_{k+1}}_{t_k}|y-y_1|^{2H-2}\mathrm {d}y\mathrm {d}y_1\right) ^{\frac{1}{2}}+\tau ^{2+\theta }\sum ^{m}_{j=1}\left\| A^{\frac{\alpha (\theta +1)}{2}}u^N(t_j)\right\| _{L^2(D,U)}. \end{aligned}$$

Combining the above estimates and Corollary 2 leads to

$$\begin{aligned} J_1\lesssim & {} \tau ^{\frac{\gamma }{\alpha }}\left( \frac{T^H}{\varepsilon }+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \\&+\left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) , \ \alpha <\gamma \le 2\alpha \end{aligned}$$

and

$$\begin{aligned} J_1\lesssim & {} \tau ^{2}\left( T^H+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \\&+\left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) ,\ \gamma >2\alpha . \end{aligned}$$

Similar to \(J_1\), one gets

$$\begin{aligned} J_2\lesssim & {} \left\| \sum ^{m}_{j=1}\frac{ A^{-\frac{\alpha }{2}}\int ^{t_{j+1}}_{t_j}\int ^{t_{j+1}}_{s}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-r)\right) \mathrm {d}r\mathrm {d}s}{\tau }\left[ f_N\left( u^N(t_{j-1})\right) \right. \right. \\&-\left. \left. f_N\left( u^N(t_j)\right) +\tau f'_N\left( u^N(t_j)\right) v^N(t_j)\right] \right\| _{L^2(D,U)}\\&+\tau \sum ^{m}_{j=1}\left\| \left[ f_N\left( u^N(t_j)\right) -f_N\left( u^N(t_{j-1})\right) -f_N\left( u^N_j\right) +f_N\left( u^N_{j-1}\right) \right] \right\| _{L^2(D,U)}\\\lesssim & {} \left\| \sum ^{m}_{j=1}\frac{ A^{-\frac{\alpha }{2}}\int ^{t_{j+1}}_{t_j}\int ^{t_{j+1}}_{s}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-r)\right) \mathrm {d}r\mathrm {d}s}{\tau }\right. \\&\times \left. \left[ \int ^{t_j}_{t_{j-1}}f'_N\left( u^N(t_j)\right) v^N(t_j)\mathrm {d}r-\int ^{t_j}_{t_{j-1}}f'_N\left( u^N(r)\right) v^N(t_j)\mathrm {d}r\right] \right\| _{L^2(D,U)}\\&+\left\| \sum ^{m}_{j=1}\frac{ A^{-\frac{\alpha }{2}}\int ^{t_{j+1}}_{t_j}\int ^{t_{j+1}}_{s}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-r)\right) \mathrm {d}r\mathrm {d}s}{\tau }\right. \\&\times \left. \left[ \int ^{t_j}_{t_{j-1}}f'_N\left( u^N(r)\right) \left( v^N(t_j)-\cos \left( A^{\frac{\alpha }{2}}(t_{j}-r)\right) v^N(r)\right) \mathrm {d}r\right] \right\| _{L^2(D,U)}\\&+\left\| \sum ^{m}_{j=1}\frac{ A^{-\frac{\alpha }{2}}\int ^{t_{j+1}}_{t_j}\int ^{t_{j+1}}_{s}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-r)\right) \mathrm {d}r\mathrm {d}s}{\tau }\right. \\&\times \left. \left[ \int ^{t_j}_{t_{j-1}}f'_N\left( u^N(r)\right) \left( \cos \left( A^{\frac{\alpha }{2}}(t_{j}-r)\right) -I\right) v^N(r)\mathrm {d}r\right] \right\| _{L^2(D,U)}\\&+\tau \sum ^{m}_{j=0}\left\| u^N(t_j)-u^N_j\right\| _{L^2(D,U)}. \end{aligned}$$

For \(\alpha <\gamma \le 2\alpha \), we have

$$\begin{aligned} J_2\lesssim & {} \tau ^{\frac{\gamma }{\alpha }}\left( \frac{T^H}{\varepsilon }+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \\+ & {} \left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) +\tau \sum ^{m}_{j=0}\left\| u^N(t_j)-u^N_j\right\| _{L^2(D,U)}. \end{aligned}$$

When \(\gamma >2\alpha \), we also have

$$\begin{aligned} J_2\lesssim & {} \tau ^{2}\left( T^H+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \\+ & {} \left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) +\tau \sum ^{m}_{j=0}\left\| u^N(t_j)-u^N_j\right\| _{L^2(D,U)}. \end{aligned}$$

Combining (C.1), \(J_1\), and \(J_2\) leads to

$$\begin{aligned}&\left\| u^N(t_{m+1})-u^N_{m+1}\right\| _{L^2(D,U)}\\&\quad \lesssim \tau ^{\frac{\gamma }{\alpha }}\left( \frac{T^H}{\varepsilon }+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \\&\qquad +\left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) ,\alpha <\gamma \le 2\alpha \end{aligned}$$

and

$$\begin{aligned}&\left\| u^N(t_{m+1})-u^N_{m+1}\right\| _{L^2(D,U)}\\&\quad \lesssim \tau ^{2}\left( T^H+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \\&\qquad +\left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) , \gamma >2\alpha . \end{aligned}$$

When \(H=\frac{1}{2}\), using the same steps in (C.1), we get

$$\begin{aligned}&\left\| u^N(t_{m+1})-u^N_{m+1}\right\| _{L^2(D,U)}\\&\lesssim I_1+I_2+\sum ^{m}_{j=0}\tau \left\| u^N(t_j)-u^N_j\right\| _{L^2(D,U)}+\tau ^2. \end{aligned}$$

For \(\alpha <\gamma \le 2\alpha \), using Corollary 2 and the assumptions of f, we obtain

$$\begin{aligned} I_1\lesssim & {} \left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \right. \nonumber \\&\times \left. \int ^s_{t_j}\left( f'_N\left( u^N(r)\right) -f'_N\left( u^N(t_j)\right) \right) v^N(r)\mathrm {d}r\mathrm {d}s\right\| _{L^2(D,U)}\nonumber \\&+\left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \right. \nonumber \\&\times \left. \int ^s_{t_j}f'_N\left( u^N(t_j)\right) \left( \cos \left( A^{\frac{\alpha }{2}}(r-t_j)\right) -I\right) v^N(t_j)\mathrm {d}r\mathrm {d}s\right\| _{L^2(D,U)}\nonumber \\&+\left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \right. \nonumber \\&\times \left. \int ^s_{t_j}f'_N\left( u^N(t_j)\right) \left( v^N(r)-\cos \left( A^{\frac{\alpha }{2}}(r-t_j)\right) v^N(t_j)\right) \mathrm {d}r\mathrm {d}s\right\| _{L^2(D,U)}\nonumber \\\lesssim & {} \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}\int ^s_{t_j}\left\| \left( u^N(r)-u^N(t_j)\right) \times v^N(r)\right\| _{L^2(D,U)}\mathrm {d}r\mathrm {d}s\nonumber \\&+\sum ^{m}_{j=1}\tau ^{\frac{\gamma -\alpha }{\alpha }}\int ^{t_{j+1}}_{t_j}\int ^s_{t_j}\left\| A^{\frac{\gamma -\alpha }{2}}v^N(t_j)\right\| _{L^2(D,U)}\mathrm {d}r\mathrm {d}s+III\nonumber \\\lesssim & {} \tau ^{\frac{\gamma }{\alpha }}\left( \frac{T^H}{\varepsilon }+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \nonumber \\&+\left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) +III. \end{aligned}$$
(C.2)

Combining the fact that \(\{\beta ^{i}_{H}(t)\}_{i\in {\mathbb {N}}}\) are mutually independent and Equation (4.10), we have

$$\begin{aligned} III= & {} \left\| \sum ^{m}_{j=1}\int ^{t_{j+1}}_{t_j}A^{-\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \int ^s_{t_j}f'_N\left( u^N(t_j)\right) \right. \\&\times \left. \left[ -A^{\frac{\alpha }{2}}\sin \left( A^{\frac{\alpha }{2}}(r-t_j)\right) u^N(t_j)+\int ^{r}_{t_j}\cos \left( A^{\frac{\alpha }{2}}(r-y)\right) f_N\left( u^N(y)\right) \mathrm {d}y\right. \right. \\&+\left. \left. \int ^{r}_{t_j}\sum ^{N_1}_{i=1}\cos \left( \lambda _i^{\frac{\alpha }{2}}(r-y)\right) \sigma _i\phi _i(x)\mathrm {d}\beta ^i(y)\right] \mathrm {d}r\mathrm {d}s\right\| _{L^2(D,U)}\\\lesssim & {} \tau ^{1+\frac{\gamma }{\alpha }}\sum ^{m}_{j=1}\left\| A^{\frac{\gamma }{2}}u^N(t_j)\right\| _{L^2(D,U)}+\tau ^2\left( T^H+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right) \\&+\left( \sum ^{m}_{j=1}\mathrm {E}\left[ \left\| \int ^{t_{j+1}}_{t_j}\sum ^{N_1}_{i=1}\lambda _i^{-\frac{\alpha }{2}}\sin \left( \lambda _i^{\frac{\alpha }{2}}(t_{m+1}-s)\right) \int ^s_{t_j}f'_N\left( u^N(t_j)\right) \right. \right. \right. \\&\times \left. \left. \left. \int ^{r}_{t_j}\cos \left( \lambda _i^{\frac{\alpha }{2}}(r-y)\right) \sigma _i\phi _i(x)\mathrm {d}\beta ^i(y)\mathrm {d}r\mathrm {d}s\right\| ^2\right] \right) ^{\frac{1}{2}}\\\lesssim & {} \tau ^{\frac{\gamma }{\alpha }}\left( \frac{T^H}{\varepsilon }+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right) . \end{aligned}$$

In first inequality, we employ the fact that Brownian motion is a process with independent increment, that is

$$\begin{aligned}&\mathrm {E}&\left[ \int ^{t_{j+1}}_{t_j}\int ^s_{t_j}f'_N\left( u^N(t_j)\right) \int ^{r}_{t_j}\cos \left( \lambda _i^{\frac{\alpha }{2}}(r-y)\right) \mathrm {d}\beta ^i(y)\mathrm {d}r\mathrm {d}s\right. \\&\times \left. \int ^{t_{k+1}}_{t_k}\int ^s_{t_k}f'_N\left( u^N(t_k)\right) \int ^{r}_{t_k}\cos \left( \lambda _i^{\frac{\alpha }{2}}(r-y)\right) \mathrm {d}\beta ^i(y)\mathrm {d}r\mathrm {d}s\right] =0,\ j\ne k. \end{aligned}$$

Then

$$\begin{aligned} I_1\lesssim & {} \tau ^{\frac{\gamma }{\alpha }}\left( \frac{T^H}{\varepsilon }+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}+\left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) . \end{aligned}$$

Similar to \(J_2\) and \(I_1\), we have

$$\begin{aligned} I_2\lesssim & {} \tau ^{\frac{\gamma }{\alpha }}\left( \frac{T^H}{\varepsilon }+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}+\left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}\right. \\&+\left. \left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) +\tau \sum ^{m}_{j=0}\left\| u^N(t_j)-u^N_j\right\| _{L^2(D,U)}. \end{aligned}$$

If \(\gamma >2\alpha \), then

$$\begin{aligned} I_1\lesssim & {} \tau ^{2}\left( T^H+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}+\left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) \end{aligned}$$

and

$$\begin{aligned} I_2\lesssim & {} \tau ^{2}\left( T^H+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}+\left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) \\&+\tau \sum ^{m}_{j=0}\left\| u^N(t_j)-u^N_j\right\| _{L^2(D,U)}. \end{aligned}$$

Using the above estimates and the discrete Grönwall inequality, we obtain

$$\begin{aligned}&\left\| u^N(t_{m+1})-u^N_{m+1}\right\| _{L^2(D,U)}\nonumber \\&\quad \lesssim \tau ^{\frac{\gamma }{\alpha }}\left( \frac{T^H}{\varepsilon }+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \nonumber \\&\qquad +\left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) , \alpha <\gamma \le 2\alpha \end{aligned}$$
(C.3)

and

$$\begin{aligned}&\left\| u^N(t_{m+1})-u^N_{m+1}\right\| _{L^2(D,U)}\nonumber \\&\quad \lesssim \tau ^{2}\left( T^H+\left\| u_0\right\| _{L^2(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| _{L^2(D,{\dot{U}}^{\gamma -\alpha })}\right. \nonumber \\&\qquad +\left. \left\| u_0\right\| ^2_{L^4(D,{\dot{U}}^\gamma )}+\left\| v_0\right\| ^2_{L^4(D,{\dot{U}}^{\gamma -\alpha })}\right) ,\gamma >2\alpha . \end{aligned}$$
(C.4)

Take \(0<\tau <1\) and \(\varepsilon =\frac{1}{|\log (\tau )|}\). Combining above estimates and Theorem 4.1, we obtain the desired results. \(\square \)

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Liu, X., Deng, W. Higher Order Approximation for Stochastic Space Fractional Wave Equation Forced by an Additive Space-Time Gaussian Noise. J Sci Comput 87, 11 (2021). https://doi.org/10.1007/s10915-021-01415-0

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