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Identification of a time-dependent control parameter for a stochastic diffusion equation

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Abstract

In this paper, we are interested in a time-dependent control parameter identification for a stochastic diffusion equation. First, we analyze the ill-posedness of the inverse problem by deriving the regularities of the solution to the direct problem in the sense of expectation. With the first moment of the realizations of average data in some sub-domain, we prove the existence and uniqueness of the identification problem. Then, the mollification regularization method is taken to regularize the inverse problem and the a prior convergence rate of the regularized solution is derived. Next, an inversion algorithm which can be paralleled is proposed to solve this inverse problem. Several numerical experiments are presented to show the efficiency of the inversion algorithm.

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References

  • Aihara S, Bagchi A (1989) Infinite-dimensional parameter identification for stochastic parabolic systems. Stat Probab Lett 8:279–287

    Article  MathSciNet  MATH  Google Scholar 

  • Al-Hussein AR (2005) Strong, mild and weak solutions of backward stochastic evolution equations. Random Oper Stoch Equ 13(2):129–138

    Article  MathSciNet  MATH  Google Scholar 

  • Brezis H (2011) Functional analysis, Sobolev spaces and partial differential equations. Springer, New York

    Book  MATH  Google Scholar 

  • Brunner H (2017) Volterra integral equations an introduction to theory and applications. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Curtain RF, Falb PL (1971) Stochastic differential equations in Hilbert space. J Differ Equ 10:412–430

    Article  MathSciNet  MATH  Google Scholar 

  • Da Prato G (1983) Some results on linear stochastic evolution equations in Hilbert spaces by the semigroups method. Stoch Anal Appl 1:57–88

    Article  MathSciNet  MATH  Google Scholar 

  • Da Prato G, Lunardi A (1998) Maximal regularity for stochastic convolutions in \(L^p\) spaces. Atti Accad Naz Lincei Cl Sci Fis Mat Natur Rend Lincei Mat Appl 9:25–29

    MathSciNet  MATH  Google Scholar 

  • Davie A, Gaines J (2001) Convergence of numerical schemes for the solution of parabolic stochastic partial differential equations. Math Comput 70:121–134

    Article  MathSciNet  MATH  Google Scholar 

  • Dehghan M (2005a) Parameter determination in a partial differential equation from the overspecified data. Math Comput Model 41(2–3):196–213

    Article  MathSciNet  MATH  Google Scholar 

  • Dehghan M (2005b) Identification of a time-dependent coefficient in a partial differential equation subject to an extra measurement. Numer Methods Partial Differ Equ Int J 21(3):611–622

    Article  MathSciNet  MATH  Google Scholar 

  • Dehghan M, Tatari M (2006) Determination of a control parameter in a one-dimensional parabolic equation using the method of radial basis functions. Math Comput Model 44(11–12):1160–1168

    Article  MathSciNet  MATH  Google Scholar 

  • Du Q, Zhang TY (2002) Numerical approximation of some linear stochastic partial differential equations driven by special additive noises. SIAM J Numer Anal 40:1421–1445

    Article  MathSciNet  MATH  Google Scholar 

  • Evans LC (2010) Partial differential equations. American Mathematical Society, Providence

    MATH  Google Scholar 

  • Feng X, Li P, Wang X (2020) An inverse random source problem for the time fractional diffusion equation driven by a fractional Brownian motion. Inverse Probl 36:045008

    Article  MathSciNet  MATH  Google Scholar 

  • Fu S, Zhang Z (2021) Application of the generalized multiscale finite element method in an inverse random source problem. J Comput Phys 429:110032

    Article  MathSciNet  Google Scholar 

  • Gyöngy I (1998) Lattice approximations for stochastic quasi-linear parabolic partial differential equations driven by space-time white noise I. Potential Anal 9:1–25

    Article  MathSciNet  MATH  Google Scholar 

  • Gyöngy I (1999) Lattice approximations for stochastic quasi-linear parabolic partial differential equations driven by space-time white noise II. Potential Anal 11:1–37

    Article  MathSciNet  MATH  Google Scholar 

  • Jentzen A, Kloeden PE (2009) The numerical approximation of stochastic partial differential equations. Milan J Math 77:205–244

    Article  MathSciNet  MATH  Google Scholar 

  • Kruse R (2014) Optimal error estimates of Galerkin finite element methods for stochastic partial differential equations with multiplicative noise. IMA J Numer Anal 34(1):217–251

    Article  MathSciNet  MATH  Google Scholar 

  • Liu D (2003) Convergence of the spectral method for stochastic Ginzburg–Landau equation driven by space-time white noise. Commun Math Sci 1:361–375

    Article  MathSciNet  MATH  Google Scholar 

  • Liu F, Khan M, Yan Y (2018) Fourier spectral methods for stochastic space fractional partial differential equations driven by special additive noises. J Comput Anal Appl 24:290–309

    MathSciNet  Google Scholar 

  • Lü Q (2012) Carleman estimate for stochastic parabolic equations and inverse stochastic parabolic problems. Inverse Probl 28:045008

  • Mohebbi A, Dehghan M (2010) High-order scheme for determination of a control parameter in an inverse problem from the over-specified data. Comput Phys Commun 181:1947–1954

    Article  MathSciNet  MATH  Google Scholar 

  • Murio DA (1993) The mollification method and the numerical solution of ill-posed problems. A Wiley-Interscience Publication, New York

    Book  Google Scholar 

  • Murio DA, Guo L (1990) Discrete stability analysis of the mollification method for numerical differentiation. Comput Math Appl 19(6):15–26

    Article  MathSciNet  MATH  Google Scholar 

  • Niu P, Helin T, Zhang Z (2020) An inverse random source problem in a stochastic fractional diffusion equation. Inverse Probl 36:045002

    Article  MathSciNet  MATH  Google Scholar 

  • Prato D, Zabczyk J (1992) Stochastic equations in infinite dimensions. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Shamsi M, Dehghan M (2012) Determination of a control function in three-dimensional parabolic equations by Legendre pseudospectral method. Numer Methods Partial Differ Equ 28:74–93

    Article  MathSciNet  MATH  Google Scholar 

  • Shivanian E, Jafarabadi A (2018) An inverse problem of identifying the control function in two and three-dimensional parabolic equations through the spectral meshless radial point interpolation. Appl Math Comput 325:82–101

    MathSciNet  MATH  Google Scholar 

  • Walsh JB (1986) An introduction to stochastic partial differential equations. Springer, Berlin

    Book  MATH  Google Scholar 

  • Yan Y (2004) Semidiscrete Galerkin approximation for a linear stochastic parabolic partial differential equation driven by an additive noise. Bit Numer Math 44:829–847

    Article  MathSciNet  MATH  Google Scholar 

  • Yan YB (2005) Galerkin finite element methods for stochastic parabolic partial differential equations. SIAM J Numer Anal 43(4):1363–1384

    Article  MathSciNet  MATH  Google Scholar 

  • Yang L, Dehghan M, Yu JN, Luo GW (2011) Inverse problem of time-dependent heat sources numerical reconstruction. Math Comput Simul 81(8):1656–1672

    Article  MathSciNet  MATH  Google Scholar 

  • Yoo H (2000) Semi-discretization of stochastic partial differential equations on R1 by a finite-difference method. Math Comput 69:653–666

    Article  MATH  Google Scholar 

  • Yousefi SA (2009) Finding a control parameter in a one-dimensional parabolic inverse problem by using the Bernstein Galerkin method. Inverse Probl Sci Eng 17:821–828

    Article  MathSciNet  MATH  Google Scholar 

  • Yuan GH (2017) Conditional stability in determination of initial data for stochastic parabolic equations. Inverse Probl 33:035014

    Article  MathSciNet  MATH  Google Scholar 

  • Zolfaghari R (2013) Parameter determination in a parabolic inverse problem in general dimensions. Comput Methods Differ Equ 1(1):55–70

    MATH  Google Scholar 

  • Zou GA (2018) Galerkin finite element method for time-fractional stochastic diffusion equations. Comput Appl Math 37(4):1–22

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (12061008, 11861007,11761007), Natural Science Foundation of Jiangxi Province of China (20202BABL201004).

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Correspondence to Zhousheng Ruan.

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Communicated by Antonio José Silva Neto.

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Ruan, Z., Hu, Q. & Zhang, W. Identification of a time-dependent control parameter for a stochastic diffusion equation. Comp. Appl. Math. 40, 201 (2021). https://doi.org/10.1007/s40314-021-01598-0

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  • DOI: https://doi.org/10.1007/s40314-021-01598-0

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