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Long-time weak convergence analysis of a semi-discrete scheme for stochastic Maxwell equations (2403.09293v1)

Published 14 Mar 2024 in math.NA and cs.NA

Abstract: It is known from the monograph [1, Chapter 5] that the weak convergence analysis of numerical schemes for stochastic Maxwell equations is an unsolved problem. This paper aims to fill the gap by establishing the long-time weak convergence analysis of the semi-implicit Euler scheme for stochastic Maxwell equations. Based on analyzing the regularity of transformed Kolmogorov equation associated to stochastic Maxwell equations and constructing a proper continuous adapted auxiliary process for the semi-implicit scheme, we present the long-time weak convergence analysis for this scheme and prove that the weak convergence order is one, which is twice the strong convergence order. As applications of this result, we obtain the convergence order of the numerical invariant measure, the strong law of large numbers and central limit theorem related to the numerical solution, and the error estimate of the multi-level Monte Carlo estimator. As far as we know, this is the first result on the weak convergence order for stochastic Maxwell equations.

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References (16)
  1. L. Kurt and T. Schäfer, “Propagation of ultra-short solitons in stochastic Maxwell’s equations,” J. Math. Phys., vol. 55, no. 1, pp. 011 503, 11, 2014. [Online]. Available: https://doi.org/10.1063/1.4859815
  2. G.-R. Liu, “Stochastic wave propagation in Maxwell’s equations,” J. Stat. Phys., vol. 158, no. 5, pp. 1126–1146, 2015. [Online]. Available: https://doi.org/10.1007/s10955-014-1148-y
  3. J. Hong, L. Ji, and L. Zhang, “A stochastic multi-symplectic scheme for stochastic Maxwell equations with additive noise,” J. Comput. Phys., vol. 268, pp. 255–268, 2014. [Online]. Available: https://doi.org/10.1016/j.jcp.2014.03.008
  4. C. Chen, J. Hong, and L. Zhang, “Preservation of physical properties of stochastic Maxwell equations with additive noise via stochastic multi-symplectic methods,” J. Comput. Phys., vol. 306, pp. 500–519, 2016. [Online]. Available: https://doi.org/10.1016/j.jcp.2015.11.052
  5. J. Hong, L. Ji, L. Zhang, and J. Cai, “An energy-conserving method for stochastic Maxwell equations with multiplicative noise,” J. Comput. Phys., vol. 351, pp. 216–229, 2017. [Online]. Available: https://doi.org/10.1016/j.jcp.2017.09.030
  6. J. Hong, B. Hou, Q. Li, and L. Sun, “Three kinds of novel multi-symplectic methods for stochastic Hamiltonian partial differential equations,” J. Comput. Phys., vol. 467, pp. Paper No. 111 453, 24, 2022. [Online]. Available: https://doi.org/10.1016/j.jcp.2022.111453
  7. B. Hou, “Meshless structure-preserving GRBF collocation methods for stochastic Maxwell equations with multiplicative noise,” Appl. Numer. Math., vol. 192, pp. 337–355, 2023. [Online]. Available: https://doi.org/10.1016/j.apnum.2023.07.001
  8. C. Chen, J. Hong, and L. Ji, “Mean-square convergence of a semidiscrete scheme for stochastic Maxwell equations,” SIAM J. Numer. Anal., vol. 57, no. 2, pp. 728–750, 2019. [Online]. Available: https://doi.org/10.1137/18M1170431
  9. ——, “Runge-Kutta semidiscretizations for stochastic Maxwell equations with additive noise,” SIAM J. Numer. Anal., vol. 57, no. 2, pp. 702–727, 2019. [Online]. Available: https://doi.org/10.1137/18M1193372
  10. D. Cohen, J. Cui, J. Hong, and L. Sun, “Exponential integrators for stochastic Maxwell’s equations driven by Itô noise,” J. Comput. Phys., vol. 410, pp. 109 382, 21, 2020. [Online]. Available: https://doi.org/10.1016/j.jcp.2020.109382
  11. C. Chen, “A symplectic discontinuous Galerkin full discretization for stochastic Maxwell equations,” SIAM J. Numer. Anal., vol. 59, no. 4, pp. 2197–2217, 2021. [Online]. Available: https://doi.org/10.1137/20M1368537
  12. J. Sun, C.-W. Shu, and Y. Xing, “Multi-symplectic discontinuous Galerkin methods for the stochastic Maxwell equations with additive noise,” J. Comput. Phys., vol. 461, pp. Paper No. 111 199, 30, 2022. [Online]. Available: https://doi.org/10.1016/j.jcp.2022.111199
  13. C. Chen, J. Hong, L. Ji, and G. Liang, “Ergodic numerical approximations for stochastic Maxwell equations,” arXiv: 2210.06092 (2022).
  14. J. Sun, C.-W. Shu, and Y. Xing, “Discontinuous Galerkin methods for stochastic Maxwell equations with multiplicative noise,” ESAIM Math. Model. Numer. Anal., vol. 57, no. 2, pp. 841–864, 2023. [Online]. Available: https://doi.org/10.1051/m2an/2022084
  15. C. Chen, T. Dang, J. Hong, and G. Song, “Probabilistic limit behaviors of numerical discretizations for time-homogeneous Markov processes,” arXiv: 2310.08227 (2023).
  16. A. Lang, “A note on the importance of weak convergence rates for SPDE approximations in multilevel Monte Carlo schemes,” in Monte Carlo and Quasi-Monte Carlo Methods, ser. Springer Proc. Math. Stat.   Springer, [Cham], 2016, vol. 163, pp. 489–505.

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