Abstract
This paper is concerned with a linearized second-order finite difference scheme for solving the nonlinear time-fractional Schrödinger equation in d (d = 1,2,3) dimensions. Under a weak assumption on the nonlinearity, the optimal error estimate of the numerical solution is established without any restriction on the grid ratio. Besides the standard energy method, the key tools for analysis include the mathematical induction method, several inverse Sobolev inequalities, and a discrete fractional Gronwall-type inequality. The convergence rate of the proposed scheme is of O(τ2 + h2) with time step τ and mesh size h. Numerical results are carried out to confirm the theoretical analysis.


Similar content being viewed by others
References
Acedo, S.B.Y.: An explicit finite difference method a new von neumann-type stability analysis for fractional diffusion equations. SIAM J. Numer. Anal. 42(5), 1862–1874 (2005)
Alikhanov, A.A.: A new difference scheme for the time fractional diffusion equation. J. Comput. Phys. 280, 424–438 (2015)
Antoine, X., Bao, W., Besse, C.: Computational methods for the dynamics of the nonlinear Schröinger/gross-Pitaevskii equations. Comput. Phys. Comm. 184, 2621–2633 (2013)
Antoine, X., Tang, Q., Zhang, J.: On the numerical solution and dynamical laws of nonlinear fractional Schrödinger/gross-Pitaevskii Equations. Int. J. Comput. Math. 95, 1423–1443 (2018)
Bao, W., Cai, Y.: Mathematical theorey and numerical methods for Bose-Einstein condensation. Kinet. Relat. Mod. 6, 1–135 (2013)
Bao, W., Carles, R., Su, C., Tang, Q.: Error estimates of a regularized finite difference method for the logarithmic Schrödinger equation. SIAM J. Numer. Anal. 57, 657–680 (2019)
Bao, W., Carles, R., Su, C., Tang, Q.: Regularized numerical methods for the logarithmic Schrödinger equation. Numer. Math. 143, 461–487 (2019)
Bhrawy, A.H., Doha, E.H., Ezz-Eldien, S.S., Van Gorder, R.A.: A new Jacobi spectral collocation method for solving 1 + 1 fractional Schrödinger equations and fractional coupled Schrödinger systems. Eur. Phys. J. plus. 129, 260 (2014)
Bhrawy, A.H., Abdelkawy, M.A.: A fully spectral collocation approximation for multidimensional fractional Schrödinger equations. J. Comput. Phys. 294, 462–483 (2015)
Cao, J., Xu, C.: A high order schema for the numerical solution of the fractional ordinary differential equations. J. Comput. Phys. 586, 93–103 (2013)
Cao, W., Zhang, Z., Karniadakis, G.E.: Time-splitting schemes for fractional differential equations I: smooth solutions. SIAM J. Sci. Comput. 37(4), A1752–A1776 (2015)
Chang, Q., Jia, E., Sun, W.: Difference schemes for solving the generalized nonlinear Schrödinger equation. J. Comput. Phys. 148, 397–415 (1999)
Chen, X., Di, Y., Duan, J., Li, D.: Linearized compact, ADI Schemes for nonlinear time-fractional Schrödinger equations. Appl. Math. Lett. 84, 160–167 (2018)
Gao, G., Sun, Z., Zhang, H.: A new fractional numerical differentiation formula to approximate the Caputo fractional derivative and its applications. J. Comput. Phys. 259, 33–50 (2014)
Gao, G., Sun, Z.: A compact finite difference scheme for the fractional sub-diffusion equations. J. Compu. Phys. 230(3), 586–595 (2011)
Henning, P., Peterseim, D.: Crank-nicolson Galerkin approximation to nonlinear Schrödinger equation with rough potentials. Math. Mod. Meth. Appl. S 27(11), 2147–2184 (2017)
Henning, P., Wrnegrd, J.: A note on optimal H1-error estimates for Crank-Nicolson approximations to the nonlinear Schrödinger equation, BIT. https://doi.org/10.1007/s10543-020-00814-3
Hicdurmaz, B., Ashyralyev, A.: A stable numerical method for multidimensional time fractional Schrödinger equations. Comput. Math. Appl. 72, 1703–1713 (2016)
Iomin, A.: Fractional-time Schrdinger equation: fractional dynamics on a comb. Chaos Soliton. Fract. 44(4-5), 348–352 (2011)
Jin, B., Lazarov, R., Zhou, Z.: Two schemes for fractional diffusion and diffusion-wave equations with nonsmooth data. SIAM J. Sci. Comput. 38(1), A146–A170 (2014)
Jin, B., Li, B., Zhou, Z.: Numerical analysis of nonlinear subdiffusion equations. SIAM J. Numer. Anal. 56(1), 1–23 (2017)
Jin, B., Li, B., Zhou, Z.: Discrete maximal regularity of time-stepping schemes for fractional evolution equations. Numer. Math. 138, 101–131 (2018)
Langlands, T.A.M., Henry, B.I.: The accuracy and stability of an implicit solution method for the fractional diffusion equation. J. Comput. Phys. 205(2), 719–736 (2005)
Li, D., Liao, H., Sun, W., Wang, J., Zhang, J.: Analysis of L1-Galerkin FEMs for time-fractional nonlinear parabolic problems. Commun. Comput. Phys. 24, 86–103 (2018)
Li, D., Wang, J., Zhang, J.: Unconditionally convergent L1-Galerkin FEMs for nonlinear time-fractional Schrödinger equations. SIAM J. Sci. Comput. 39(6), A3067–A3088 (2017)
Li, X., Cai, Y., Wang, P.: Operator-compensation methodswith mass and energy conservation for solving the Gross-Pitaevskii equation. Appl. Numer. Math. 151, 337–353 (2020)
Li, X., Zhu, J., Zhang, R., Cao, S.: A combined discontinuous Galerkin method for the dipolar Bose-Einstein condensation. J. Comput. Phys. 275, 363–376 (2014)
Liao, H., Mclean, W., Zhang, J.: A discrete grönwall inequality with applications to numerical schemes for subdiffusion problems. SIAM J. Numer. Anal. 57(1), 218–237 (2019)
Liao, H., Mclean, W., Zhang, J.: A second-order scheme with nonuniform time steps for a linear reaction-subdiffusion problem. Commun. Comput. Phys. 30(2), 567–601 (2021)
Liao, H. , Tang, T. , Zhou, T.: A second-order and nonuniform time-stepping maximum-principle preserving scheme for time-fractional Allen-Cahn equations. J. Comput. Phys. 414, 109473 (2020)
Lubich, C.: On splitting methods for schrödinger-poisson and cubic nonlinear Schrödinger equations. Math. Comp. 77, 2141–2153 (2008)
Ji, B., Liao, H., Gong, Y., Zhang, L.: Adaptive second-order Crank–Nicolson time-stepping schemes for time-fractional molecular beam epitaxial growth models. SIAM J. Sci. Comput. 42(3), B738–B760 (2020)
Lin, Y., Xu, C.: Finite difference/spectral approximations for the time-fractional diffusion equation. J. Comput. Phys. 225, 1533–1552 (2007)
Mohebbi, A., Abbaszadeh, M., Dehghan, M.: The use of a meshless technique based on collocation and radial basis functions for solving the time fractional nonlinear Schröinger equation arising in quantum mechanic. Eng. Anal. Bound Elem. 37(2), 475–485 (2013)
Mustapha, K., Almutaw, J.: A finite difference method for an anomalous sub-diffusion equation, theory and applications. Numer. Algorithms 61 (4), 525–543 (2012)
Mustapha, K.: Time-stepping discontinuous Galerkin methods for fractional diffusion problems. Numer. Math. 130(3), 497–516 (2015)
Mustapha, K., McLean, W.: Superconvergence of a discontinuous Galerkin method for fractional diffusion and wave equations. SIAM J. Numer. Anal. 51(1), 491–515 (2013)
Naber, M.: Time fractional Schrödinger equation. J. Math. Phys. 45, 3339–3352 (2004)
Ohannes, K., Charalambos, M. : A space-time finite element method for the nonlinear Schrödinger equation: the continuous Galerkin method. SIAM J. Numer. Anal. 36, 1779–1807 (1999)
Sanz-Serna, J.M.: Methods for the numerical solution of the nonlinear Schrödinger equation. Math. Comput. 43, 21–27 (1984)
Sun, Z., Wu, X.: A fully discrete difference scheme for a diffusion-wave system. Appl. Numer. Math. 56(2), 193–209 (2006)
Thalhammer, M.: High-order exponential operator splitting methods for timedependent Schrödinger equations. SIAM J. Numer. Anal. 46, 2022–2038 (2008)
Thalhammer, M., Caliari, M., Neuhauser, C.: High-order time-splitting Hermite and Fourier spectral methods. J. Comput. Phys. 228, 822–832 (2009)
Tofighi, A.: Probability structure of time fractional Schröinger equation. Acta. Physica Polonica Series A. 116(2), 114–119 (2009)
Wang, J.: A new error analysis of Crank-Nicolson Galerkin FEMs for a generalized nonlinear Schrödinger equation. J. Sci. Comput. 60, 390–407 (2014)
Wang, S., Xu, M.: Generalized fractional schrödinger equation with space-time fractional derivatives. J. Math. Phys. 48(4), 81 (2007)
Wang, T., Wang, J., Guo, B.: Two completely explicit and unconditionally convergent Fourier pseudo-spectral methods for solving the nonlinear Schrödinger equation. J. Comput. Phys. 404, 109116 (2020)
Wang, Y., Wang, G., Bu, L., Mei, L.: Two second-order and linear numerical schemes for the multi-dimensional nonlinear time-fractional Schrödinger equation. Numer. Algorithms. https://doi.org/10.1007/s11075-020-01044-y
Wei, L., He, Y., Zhang, X.: Analysis of an implicit fully discrete local discontinuous Galerkin method for the time-fractional Schrödinger equation. Finite Elem. Anal. Des. 59, 28–34 (2012)
Xu, Y., Shu, C.-W.: Local discontinuous Galerkin methods for nonlinear Schrödinger equations. J. Comput. Phys. 205, 72–77 (2005)
Yang, Y., Wang, J., Zhang, S., Tohidi, E.: Convergence analysis of space-time Jacobi spectral collocation method for solving time-fractional Schrdinger equations. Appl. Math. Comput. 387, 124489 (2019)
Zhao, X.: Numerical integrators for continuous disordered nonlinear Schrödinger equation. J. Sci. Comput. 89, 40 (2021)
Zhou, Y.: Application of Discrete Functional Analysis to the Finite Difference Methods. International Academic Publishers, Beijing (1990)
Zhuang, P., Liu, F., Anh, V., Turner, I.: Stability and convergence of an implicit numerical method for the non-linear fractional reaction-subdiffusion process. IMA J. Appl. Math. 74(5), 645–667 (2009)
Funding
This work is supported by the National Natural Science Foundation of China (Grant No. 11571181) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20171454).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declare no competing interests.
Additional information
Data availability
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix. Proof of the time-fractional Gronwall inequality given in Lemma 3.1
Appendix. Proof of the time-fractional Gronwall inequality given in Lemma 3.1
In this appendix, we present two useful lemmas which are main tools used for proving Lemma 3.1.
Lemma A.1
Let {pn} be a sequence defined by
Then it holds that
Proof
(i) Since \( C_{0}^{\sigma } \geq C_{1}^{\sigma } \geq {\cdots } \geq C_{j}^{\sigma } \ge 0 \) for j ≥ 0, it is easy to verify inductively from (A.1) that \(0 \leq p_{n} \leq 1/C_{0}^{\sigma } \ (n \geq 1) \) by mathematical induction. Moreover, we have
This implies \( {\Phi }_{n} = {\Phi }_{0} = p_{0} C_{0}^{\sigma } = 1 \) for n ≥ 1. Substituting j = l + k − 1, we further find
The equality (A.2) is proved.
(ii) To prove (A.3) and (A.4), we introduce an auxiliary function q(t) = tmα/Γ(1 + mα) for m ≥ 1. Then for j ≥ 1, we have
Let Q(t) be a quadratic interpolation of q(t) using the points (s − 1,q(s − 1)), (s,q(s)), (s + 1,q(s + 1)) for 1 ≤ s ≤ j, and a linear interpolation of q(t) using the points (j,q(j)), (j + 1,q(j + 1)). We define the approximation error by
where
Combining (A.7) and (A.8) yields
Noting that \( q^{\prime \prime \prime }(t) \geq 0 \) for m = 1, we have \( {R_{k}^{j}} \leq 0 \) and
so we have
Multiplying (A.13) by Γ(2 − α)pn−j and summing it over for j from 0 to n, we have
where we the equality (A.2) was used.
(iii) Consider of (A.11), we have
We multiply (A.15) by Γ(2 − α)pn−j+ 1 and sum the resulting inequality for j from 1 to n to obtain
If 1 ≤ m ≤ 1/α, \( q^{\prime \prime \prime }(t) \geq 0 \), then \( {R_{k}^{j}} \leq 0 \) and \( R_{\sigma }^{j} \le 0 \), so (A.4) follows immediately from the above estimate. If m ≥ 1/α, by (A.8), we have
and
so that
Because of
and
one can immediately get (A.4), and the proof of Lemma A.1 is completed. □
Lemma A.2
Let \( \vec {e} = (1,1,\cdots ,1)^{T} \in R^{n+1} \) and
Then, it holds that
Proof
The proof is similar to that of Lemma 3.3 in [24], and we here omit it for brevity. □
We now turn back to the proof of Lemma 3.1
Proof Proof of Lemma 3.1
By the definition of \( D_{\sigma }^{\alpha } \), we get
Multiplying inequality (A.26) by pn−j and summing over for j from 1 to n, we have
By using the result (A.2) in Lemma A.1, we obtain
It follows that
Because of
and
we get
It follows that
when τ ≤ τ∗. By using the result (A.3) in Lemma A.1, we obtain
It follows that
where
and it is easy to get that Ψn ≥Ψk for n ≥ k ≥ 1. Let V = (ωn+ 1,ωn,⋯ ,ω1)T, then (A.33) can be written in a vector form by
where
By (A.1), we have
therefore,
which shows that
where J is defined in (A.22) with \( \lambda = 6 \lambda _{1} + \frac {C_{0}^{\sigma } \lambda _{2}}{C_{0}^{\sigma } - C_{1}^{\sigma }} + \frac {C_{0}^{\sigma } \lambda _{3}}{C_{1}^{\sigma } - C_{2}^{\sigma }} \). As a result, we see that
This together with Lemma A.2 completes the proof. □
Rights and permissions
About this article
Cite this article
Liu, J., Wang, T. & Zhang, T. A second-order finite difference scheme for the multi-dimensional nonlinear time-fractional Schrödinger equation. Numer Algor 92, 1153–1182 (2023). https://doi.org/10.1007/s11075-022-01335-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11075-022-01335-6
Keywords
- Nonlinear time-fractional Schrödinger equation
- Finite difference method
- Unconditional convergence
- Optimal error estimate