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Two-Level Space–Time Domain Decomposition Methods for Three-Dimensional Unsteady Inverse Source Problems

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Abstract

As the number of processor cores on supercomputers becomes larger and larger, algorithms with high degree of parallelism attract more attention. In this work, we propose a two-level space–time domain decomposition method for solving an inverse source problem associated with the time-dependent convection–diffusion equation in three dimensions. We introduce a mixed finite element/finite difference method and a one-level and a two-level space–time parallel domain decomposition preconditioner for the Karush–Kuhn–Tucker system induced from reformulating the inverse problem as an output least-squares optimization problem in the entire space-time domain. The new full space–time approach eliminates the sequential steps in the optimization outer loop and the inner forward and backward time marching processes, thus achieves high degree of parallelism. Numerical experiments validate that this approach is effective and robust for recovering unsteady moving sources. We will present strong scalability results obtained on a supercomputer with more than 1000 processors.

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References

  1. Aitbayev, R., Cai, X.-C., Paraschivoiu, M.: Parallel two-level methods for three-dimensional transonic compressible flow simulations on unstructured meshes. In: Proceedings of Parallel CFD’99 (1999)

  2. Akcelik, V., Biros, G., Draganescu, A., Ghattas, O., Hill, J., Waanders, B.: Dynamic data-driven inversion for terascale simulations: real-time identification of airborne contaminants. In: Proceedings of Supercomputing, Seattle, WA (2005)

  3. Akcelik, V., Biros, G., Ghattas, O., Long, K.R., Waanders, B.: A variational finite element method for source inversion for convective–diffusive transport. Finite Elem. Anal. Des. 39, 683–705 (2003)

    Article  MathSciNet  Google Scholar 

  4. Atmadja, J., Bagtzoglou, A.C.: State of the art report on mathematical methods for groundwater pollution source identification. Environ. Forensics 2, 205–214 (2001)

    Article  Google Scholar 

  5. Baflico, L., Bernard, S., Maday, Y., Turinici, G., Zerah, G.: Parallel-in-time molecular-dynamics simulations. Phys. Rev. E. 66, 2–5 (2002)

    Google Scholar 

  6. Balay, S., Buschelman, K., Eijkhout, V., Gropp, W.D., Kaushik, D., Knepley, M.G., McInnes, L.C., Smith, B.F., Zhang, H.: PETSc users manual. In: Technical Report, Argonne National Laboratory (2014)

  7. Battermann, A.: Preconditioners for Karush–Kuhn–Tucker Systems Arising in Optimal Control. In: Master Thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia (1996)

  8. Biros, G., Ghattas, O.: Parallel preconditioners for KKT systems arising in optimal control of viscous incompressible flows. In: Proceedings of Parallel CFD’99, Williamsburg, Virginia, USA (1999)

  9. Cai, X.-C., Liu, S., Zou, J.: Parallel overlapping domain decomposition methods for coupled inverse elliptic problems. Commun. Appl. Math. Comput. Sci. 4, 1–26 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  10. Cai, X.-C., Sarkis, M.: A restricted additive Schwarz preconditioner for general sparse linear systems. SIAM J. Sci. Comput. 21, 792–797 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chen, R.L., Cai, X.-C.: Parallel one-shot Lagrange–Newton–Krylov–Schwarz algorithms for shape optimization of steady incompressible flows. SIAM J. Sci. Comput. 34, 584–605 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  12. Deng, X.M., Cai, X.-C., Zou, J.: A parallel space–time domain decomposition method for unsteady source inversion problems. Inverse Probl. Imag. (2015)

  13. Deng, X.M., Zhao, Y.B., Zou, J.: On linear finite elements for simultaneously recovering source location and intensity. Int. J. Numer. Anal. Model. 10, 588–602 (2013)

    MathSciNet  MATH  Google Scholar 

  14. Engl, H.W., Hanke, M., Neubauer, A.: Regularization of Inverse Problems. Kluwer Academic Publishers, Dordrecht (1998)

    MATH  Google Scholar 

  15. Farhat, C., Chandesris, M.: Time-decomposed parallel time-integrators: theory and feasibility studies for fluid, structure, and fluid-structure applications. Int. J. Numer. Methods Eng. 58, 1397–1434 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  16. Gander, M.J., Hairer, E.: Nonlinear convergence analysis for the parareal algorithm. In: Proceedings of the 17th International Conference on Domain Decomposition Methods, vol. 60, pp. 45–56 (2008)

  17. Gander, M.J., Petcu, M.: Analysis of a Krylov subspace enhanced parareal algorithm for linear problems. In: Cances E. et al. (eds.) Paris-Sud Working Group on Modeling and Scientific Computing 2007–2008. ESAIM Proceedings of EDP Science, LesUlis, vol. 25, pp. 114–129 (2008)

  18. Gander, M.J., Vandewalle, S.: Analysis of the parareal time-parallel time-integration method. SIAM J. Sci. Comput. 29, 556–578 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  19. Gorelick, S., Evans, B., Remson, I.: Identifying sources of groundwater pollution: an optimization approach. Water Resour. Res. 19, 779–790 (1983)

    Article  Google Scholar 

  20. Hamdi, A.: The recovery of a time-dependent point source in a linear transport equation: application to surface water pollution. Inverse Probl. 24, 1–18 (2009)

    MathSciNet  MATH  Google Scholar 

  21. Karalashvili, M., Groß, S., Marquardt, W., Mhamdi, A., Reusken, A.: Identification of transport coefficient models in convection–diffusion equations. SIAM J. Sci. Comput. 33, 303–327 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  22. Keung, Y.L., Zou, J.: Numerical identifications of parameters in parabolic systems. Inverse Probl. 14, 83–100 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  23. Kuhn, H.W., Tucker, A.W.: Nonlinear programming. In: Proceedings of 2nd Berkeley Symposium. University of California Press, Berkeley, pp. 481–492 (1951)

  24. Lions, J.-L., Maday, Y., Turinici, G.: A parareal in time discretization of PDE’s. C. R. Acad. Sci. Ser. I Math. 332, 661–668 (2001)

    MathSciNet  MATH  Google Scholar 

  25. Liu, X., Zhai, Z.: Inverse modeling methods for indoor airborne pollutant tracking literature review and fundamentals. Indoor Air 17, 419–438 (2007)

    Article  MathSciNet  Google Scholar 

  26. Maday, Y., Turinici, G.: The parareal in time iterative solver: a further direction to parallel implementation. Domain Decompos. Methods Sci. Eng. 40, 441–448 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  27. Nilssen, T.K., Karlsen, K.H., Mannseth, T., Tai, X.-C.: Identification of diffusion parameters in a nonlinear convection–diffusion equation using the augmented Lagrangian method. Comput. Geosci. 13, 317–329 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  28. Prudencio, E., Byrd, R., Cai, X.-C.: Parallel full space SQP Lagrange–Newton–Krylov–Schwarz algorithms for PDE-constrained optimization problems. SIAM J. Sci. Comput. 27, 1305–1328 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  29. Revelli, R., Ridolfi, L.: Nonlinear convection–dispersion models with a localized pollutant source II—-a class of inverse problems. Math. Comput. Model. 42, 601–612 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  30. Saad, Y.: A flexible inner–outer preconditioned GMRES algorithm. SIAM J. Sci. Comput. 14, 461–469 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  31. Samarskii, A.A., Vabishchevich, P.N.: Numerical Methods for Solving Inverse Problems of Mathematical Physics. Walter de Gruyter, Berlin (2007)

    Book  MATH  Google Scholar 

  32. Skaggs, T., Kabala, Z.: Recovering the release history of a groundwater contaminant. Water Resour. Res. 30, 71–80 (1994)

    Article  Google Scholar 

  33. Smith, B., Bjørstad, P., Gropp, W.: Domain Decomposition: Parallel Multilevel Methods for Elliptic Partial Differential Equations. Cambridge University Press, Cambridge (2004)

    MATH  Google Scholar 

  34. Wong, J., Yuan, P.: A FE-based algorithm for the inverse natural convection problem. Int. J. Numer. Methods. Fluid 68, 48–82 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  35. Woodbury, K.A.: Inverse Engineering Handbook. CRC Press, Boca Raton (2003)

    MATH  Google Scholar 

  36. Yang, X.-H., She, D.-X., Li, J.-Q.: Numerical approach to the inverse convection-diffusion problem. In: 2007 International Symposium on Nonlinear Dynamics (2007 ISND), Journal of Physics: Conference Series, vol. 96,p. 012156 (2008)

  37. Yang, L., Deng, Z.-C., Yu, J.-N., Luo, G.-W.: Optimization method for the inverse problem of reconstructing the source term in a parabolic equation. Math. Comput. Simul. 80, 314–326 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  38. Yang, H., Prudencio, E., Cai, X.-C.: Fully implicit Lagrange–Newton–Krylov–Schwarz algorithms for boundary control of unsteady incompressible flows. Int. J. Numer. Methods Eng. 91, 644–665 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  39. Zhang, J., Delichatsios, M.A.: Determination of the convective heat transfer coefficient in three-dimensional inverse heat conduction problems. Fire Saf. J. 44, 681–690 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous referees for their insightful comments and suggestions that helped us improve the quality of the paper. The work was partly supported by NSFC 11501545, 91330111, Shenzhen Program JCYJ20140901003939012, KQCX20130628112914303, 201506303000093 and 863 Program 2015AA01A302. The second author was partly support by NSF CCF-1216314. The third author was substantially supported by Hong Kong RGC Grants 404611 and 405513.

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Correspondence to Xiao-Chuan Cai.

The Discrete Structure of the KKT System

The Discrete Structure of the KKT System

The KKT system (8)–(9) is formulated as follows:

$$\begin{aligned} {\left\{ \begin{array}{ll} \left( \partial _{\tau } C_h^n, v_h\right) + \left( a\nabla \bar{C}_h^n, \nabla v_h\right) + \left( \nabla \cdot \left( \mathbf {v}\bar{C}_h^n\right) ,v_h\right) = \left( \bar{f}^n_h, v_h\right) +\langle \bar{q}^n,v_h\rangle _{\varGamma _2}, ~~\forall \,v_h\in \mathring{V}^h\,\\ -\left( \partial _{\tau } G_h^n, w_h\right) + \left( a\nabla \bar{G}_h^n, \nabla w_h\right) + \left( \nabla \cdot \left( \mathbf {v}w_h\right) ,\bar{G}_h^n\right) \\ =-\left( A(\mathbf{x})\left( \bar{C}_h^{n}(\mathbf{x})-\bar{C}^{\varepsilon ,n}(\mathbf{x})\right) , w_h\right) , ~~\forall w_h\in \mathring{V}^h \,\\ - \left( G_h^n, g_h^{\tau }\right) +\beta _1\left( \partial _{\tau } f_h^n,\partial _{\tau } g_h^{\tau }\right) + \beta _2 \left( \nabla f_h^n, \nabla g_h^{\tau }\right) =0, ~~\forall \,g_h^{\tau }\in W_h^{\tau }. \end{array}\right. } \end{aligned}$$
(16)

To better understand the discrete structure of (16), we denote the identity and zero matrices as I and \(\mathbf {0}\) respectively, and the basis functions of the finite element spaces \(V^h\) and \(W_h^{\tau }\) by \(\phi =(\phi _i)^T,\,i=1,\,\ldots ,\,N\) and \(g_j^n,\,j=1,\,\ldots ,\,N,\,n=0,\,\ldots ,\,M\), respectively, let

$$\begin{aligned}&A=(a_{ij})_{i,j=1,\ldots ,N},\quad a_{ij}=(a \nabla \phi _i, \nabla \phi _j)\\&B=(b_{ij})_{i,j=1,\ldots ,N},\quad b_{ij}=( \phi _i, \phi _j)\\&E=(e_{ij})_{i,j=1,\ldots ,N},\quad e_{ij}=(\nabla \cdot (\mathbf {v} \phi _i), \phi _j)\\&L^{mn}=(l_{ij}^{mn})_{i,j=1,\ldots ,N, 0\le m, n\le M},\quad l_{ij}^{mn}=\left( \displaystyle \frac{\partial g_i^m}{\partial t}, \displaystyle \frac{\partial g_j^n}{\partial t}\right) \\&K^{mn}=(k_{ij}^{mn})_{i,j=1,\ldots ,N, 0\le m, n\le M},\quad k_{ij}^{mn}=\big ( \nabla g_i^m, \nabla g_j^n\big )\\&D^{mn}= (d_{ij}^{mn})_{i,j=1,\ldots ,N, 0\le m, n\le M},\quad d_{ij}^{mn}=\big ( g_i^m, g_j^n\big ), \end{aligned}$$

and based on these element matrices we define

$$\begin{aligned}&A_1=B+\displaystyle \frac{\tau }{2}(A+E),\quad A_2=-B+\displaystyle \frac{\tau }{2}(A+E) \\&B_1=B+\displaystyle \frac{\tau }{2}\big (A+E^T\big ),\quad B_2 =-B+\displaystyle \frac{\tau }{2}\big (A+E^T\big )\\&B_3= \text{ zeros } \text{ except } \text{1 } \text{ at } \text{ the } \text{ measurement } \text{ locations }\quad \quad \quad \\&W^{mn}=\beta _1 L^{mn}+ \beta _2 K^{mn}, \end{aligned}$$

Then the system (16) takes the following form

$$\begin{aligned} \left( \begin{array}{ccc}BC&BG&Bf\end{array}\right) \left( \begin{array}{c}C^0\\ C^1\\ \vdots \\ C^{M-2}\\ C^{M-1}\\ C^{M}\\ G^0\\ G^1\\ G^2\\ \vdots \\ G^{M-2}\\ G^{M-1}\\ G^{M}\\ f^0\\ f^1\\ f^2\\ \vdots \\ f^{M-2}\\ f^{M-1}\\ f^{M}\\ \end{array}\right) =\left( \begin{array}{c}C^0\\ \langle \bar{q}^1,\phi \rangle _{\varGamma _2} \\ \vdots \\ \langle \bar{q}^{M-1},\phi \rangle _{\varGamma _2} \\ \langle \bar{q}^{M},\phi \rangle _{\varGamma _2}\\ \tau /2 B_3 \big (C^{\varepsilon ,0}+ C^{\varepsilon ,1}\big )\\ \vdots \\ \tau /2 B_3 \big (C^{\varepsilon ,M-2}+ C^{\varepsilon ,M-1}\big )\\ \tau /2 B_3 \big (C^{\varepsilon ,M-1}+ C^{\varepsilon ,M}\big )\\ G^M \\ 0\\ 0\\ \vdots \\ 0\\ 0\end{array}\right) , \end{aligned}$$

where the block matrices BCBG and Bf are given by

$$\begin{aligned} BC:= & {} \left( \begin{array}{c@{\quad }c@{\quad }c@{\quad }c@{\quad }c@{\quad }c} I&{} \mathbf {0}&{} \cdots &{} \mathbf {0}&{}\mathbf {0}&{} \mathbf {0}\\ A_2&{}A_1&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{} \mathbf {0}\\ \mathbf {0}&{}\ddots &{} \ddots &{} \mathbf {0} &{}\mathbf {0}&{} \mathbf {0}\\ \mathbf {0}&{}\mathbf {0} &{} \ddots &{} A_2 &{} A_1&{} \mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{} \mathbf {0}&{} A_2 &{} A_1\\ \frac{\tau }{2} B_3&{}\frac{\tau }{2} B_3&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\ddots &{} \ddots &{}\mathbf {0} &{} \mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{} \frac{\tau }{2} B_3 &{} \frac{\tau }{2} B_3&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{} \frac{\tau }{2} B_3 &{} \frac{\tau }{2} B_3\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\end{array},\right) \\ BG:= & {} \left( \begin{array}{c@{\quad }c@{\quad }c@{\quad }c@{\quad }c@{\quad }c@{\quad }c} \mathbf {0}&{}\mathbf {0}&{} \mathbf {0}&{} \cdots &{} \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{} \cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ B_1&{}B_2&{}\mathbf {0}&{} \cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\ddots &{} \ddots &{}\cdots &{}\mathbf {0}&{} \mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{}\cdots &{}B_1&{}B_2&{}\mathbf {0}\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}B_1&{}B_2\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}I\\ -D^{00}&{} -D^{01}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ -D^{10}&{} -D^{11}&{} -D^{12}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\ddots &{} \ddots &{} \ddots &{} \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{}\cdots &{}-D^{M-1,M-2}&{}-D^{M-1,M-1}&{}-D^{M-1,M}\\ \mathbf {0}&{} \mathbf {0}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}-D^{M,M-1}&{}-D^{MM}\end{array}\right) \end{aligned}$$
$$\begin{aligned} Bf := \left( \begin{array}{c@{\quad }c@{\quad }c@{\quad }c@{\quad }c@{\quad }c@{\quad }c} \mathbf {0}&{}\mathbf {0}&{} \mathbf {0}&{} \cdots &{} \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ -\frac{\tau }{2} B&{}-\frac{\tau }{2} B&{}\mathbf {0}&{}\cdots &{} \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{} \ddots &{} \ddots &{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{} \mathbf {0} &{} \ddots &{}\cdots &{}-\frac{\tau }{2} B&{}-\frac{\tau }{2} B&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}-\frac{\tau }{2} B&{}-\frac{\tau }{2} B\\ \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}&{}\cdots &{} \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ W^{00}&{} W^{01}&{} \mathbf {0}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ W^{10}&{} W^{11}&{} W^{12}&{}\cdots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\ddots &{} \ddots &{} \ddots &{}\mathbf {0}&{}\mathbf {0}&{}\mathbf {0}\\ \mathbf {0}&{}\mathbf {0}&{} \mathbf {0}&{} \cdots &{}W^{M-1,M-2}&{}W^{M-1,M-1}&{}W^{M-1,M}\\ \mathbf {0}&{}\mathbf {0}&{}\mathbf {0}&{}\cdots &{}\mathbf {0} &{}W^{M,M-1}&{}W^{MM}\end{array}\right) . \end{aligned}$$

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Deng, X., Cai, XC. & Zou, J. Two-Level Space–Time Domain Decomposition Methods for Three-Dimensional Unsteady Inverse Source Problems. J Sci Comput 67, 860–882 (2016). https://doi.org/10.1007/s10915-015-0109-1

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