Research article Topical Sections

Numerical analysis of the MLMC ensemble scheme for transient heat equations with uncertain inputs

  • Received: 02 June 2025 Revised: 18 August 2025 Accepted: 22 August 2025 Published: 28 August 2025
  • MSC : 65C05, 65M60, 65N12

  • A multilevel Monte Carlo ensemble (MLMCE) coupled with the finite element method is applied to address numerically transient heat equations characterized by random diffusion and Robin coefficients. By incorporating two ensemble averages for the Robin boundary and diffusion coefficients, we present an extended Monte Carlo ensemble scheme tailored for the uncertain transient heat equation. The suggested MLMCE approach resolves a single linear system that entails multiple right-hand-side vectors for a group during each time step, thereby decreasing both the storage requirements and the computational expenses associated with the solution process. Stability analysis and error estimates of the method are derived under some conditions involving two ratios between fluctuations in the thermal conductivity and a random Robin coefficient corresponding to their mean. Numerical experiments are presented to confirm the theoretical results and verify the feasibility and effectiveness of the proposed approach.

    Citation: Tingfu Yao, Changlun Ye, Xianbing Luo. Numerical analysis of the MLMC ensemble scheme for transient heat equations with uncertain inputs[J]. AIMS Mathematics, 2025, 10(8): 19816-19844. doi: 10.3934/math.2025884

    Related Papers:

  • A multilevel Monte Carlo ensemble (MLMCE) coupled with the finite element method is applied to address numerically transient heat equations characterized by random diffusion and Robin coefficients. By incorporating two ensemble averages for the Robin boundary and diffusion coefficients, we present an extended Monte Carlo ensemble scheme tailored for the uncertain transient heat equation. The suggested MLMCE approach resolves a single linear system that entails multiple right-hand-side vectors for a group during each time step, thereby decreasing both the storage requirements and the computational expenses associated with the solution process. Stability analysis and error estimates of the method are derived under some conditions involving two ratios between fluctuations in the thermal conductivity and a random Robin coefficient corresponding to their mean. Numerical experiments are presented to confirm the theoretical results and verify the feasibility and effectiveness of the proposed approach.



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    [1] I. Babu$\check{s}$ka, F. Nobile, R. Tempone, A stochastic collocation method for elliptic partial differential equations with random input data, SIAM J. Numer. Anal., 52 (2010), 317–355. https://doi.org/10.1137/10078635 doi: 10.1137/10078635
    [2] S. C. Brenner, L. R. Scott, The mathematical theory of finite element methods, New York: Springer, 2008. https://doi.org/10.1007/978-0-387-75934-0
    [3] R. Chiba, Stochastic analysis of heat conduction and thermal stresses in solids: a review, IntechOpen, 2012. https://doi.org/10.5772/50994
    [4] R. Chiba, Stochastic heat conduction analysis of a functionally graded annular disc with spatially random heat transfer coefficients, Appl. Math. Model., 33 (2009), 507–523. https://doi.org/10.1016/j.apm.2007.11.014 doi: 10.1016/j.apm.2007.11.014
    [5] K. A. Cliffe, M. B. Giles, R. Scheichl, A. L. Teckentrup, Multilevel Monte Carlo methods and applications to elliptic PDEs with random coefficients, Comput. Visual Sci., 14 (2011), 3. https://doi.org/10.1007/s00791-011-0160-x doi: 10.1007/s00791-011-0160-x
    [6] L. C. Evans, Partial differential equations, American Mathematical Society, 2010.
    [7] X. Feng, Y. Luo, L. Vo, Z. Wang, An efficient iterative method for solving parameter-dependent and random convection-diffusion problems, J. Sci. Comput., 90 (2022), 72. https://doi.org/10.1007/s10915-021-01737-z doi: 10.1007/s10915-021-01737-z
    [8] G. S. Fishman, Monte Carlo: Concepts, algorithms, and applications, New York: Springer, 1996. https://doi.org/10.1007/978-1-4757-2553-7
    [9] B. Ganapathysubramanian, N. Zabaras, Sparse grid collocation schemes for stochastic natural convection problems, J. Comput. Phys., 225 (2007), 652–685. https://doi.org/10.1016/j.jcp.2006.12.014 doi: 10.1016/j.jcp.2006.12.014
    [10] M. B. Giles, Multilevel Monte Carlo methods, Acta Numer., 24 (2015), 259–328. https://doi.org/10.1017/S096249291500001X doi: 10.1017/S096249291500001X
    [11] G. H. Golup, C. F. Van Loan. Matrix computation, Baltimore: The Johns Hopkins University Press, 2013.
    [12] M. Gunzburger, C. Webster, G. Zhang, Stochastic finite element methods for partial differential equations with random input data, Acta Numer., 23 (2014), 521–650. https://doi.org/10.1017/S0962492914000075 doi: 10.1017/S0962492914000075
    [13] J. C. Helton, F. J. Davis, Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliab. Eng. Syst. Safe., 81 (2003), 23–69. https://doi.org/10.1016/S0951-8320(03)00058-9 doi: 10.1016/S0951-8320(03)00058-9
    [14] N. Jiang, W. Layton, An algorithm for fast calculation of flow ensembles, Int. J. Uncertain. Quan., 4 (2014), 273–301. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2014007691 doi: 10.1615/Int.J.UncertaintyQuantification.2014007691
    [15] B. Jin, J. Zou, Numerical estimation of the Robin coefficient in a stationary diffusion equation, IMA J. Numer. Anal., 30 (2010), 677–701. https://doi.org/10.1093/imanum/drn066 doi: 10.1093/imanum/drn066
    [16] B. Jin, X. Lu, Numerical identification of a Robin coefficient in parabolic problems, Math. Comp., 81 (2012), 1369–1398. https://doi.org/10.1090/S0025-5718-2012-02559-2 doi: 10.1090/S0025-5718-2012-02559-2
    [17] L. Ju, W. Leng, Z. Wang, S. Yuan, Numerical investigation of ensemble methods with block iterative solvers for evolution problems, Discrete Contin. Dyn. B, 25 (2020), 4905–4923. https://doi.org/10.3934/dcdsb.2020132 doi: 10.3934/dcdsb.2020132
    [18] M. Li, X. Luo, An EMC-HDG scheme for the convection-diffusion equation with random diffusivity, Numer. Algor., 90 (2022), 1755–1776. https://doi.org/10.1007/s11075-021-01250-2 doi: 10.1007/s11075-021-01250-2
    [19] M. Li, X. Luo, An MLMCE-HDG method for the convection diffusion equation with random diffusivity, Comput. Math. Appl., 127 (2022), 127–143. https://doi.org/10.1016/j.camwa.2022.10.002 doi: 10.1016/j.camwa.2022.10.002
    [20] M. Li, X. Luo, An ensemble Monte Carlo HDG method for parabolic PDEs with random coefficients, Int. J. Comput. Math., 100 (2023), 405–421. https://doi.org/10.1080/00207160.2022.2119082 doi: 10.1080/00207160.2022.2119082
    [21] M. Li, X. Luo, A multilevel Monte Carlo ensemble and hybridizable discontinuous Galerkin method for a stochastic parabolic problem, Numer. Meth. Part. D. E., 39 (2023), 2840–2864. https://doi.org/10.1002/num.22990 doi: 10.1002/num.22990
    [22] X. Lin, M. Ng, An all-at-once preconditioner for evolutionary partial differential equations, SIAM J. Sci. Comput., 43 (2021), A2766–A2784. https://doi.org/10.1137/20M1316354 doi: 10.1137/20M1316354
    [23] K. Liu, B. M. Riviere, Discontinuous galerkin methods for elliptic partial differential equations with random coefficients, Int. J. Comput. Math., 90 (2013), 2477–2490. https://doi.org/10.1080/00207160.2013.784280 doi: 10.1080/00207160.2013.784280
    [24] G. J. Lord, C. E. Powell, T. Shardlow, An introduction to computational stochastic PDEs, New York: Cambridge University Press, 2014. https://doi.org/10.1017/CBO9781139017329
    [25] Y. Luo, Z. Wang, An ensemble algorithm for numerical solutions to deterministic and random parabolic PDEs, SIAM J. Numer. Anal., 56 (2018), 859–876. https://doi.org/10.1137/17M1131489 doi: 10.1137/17M1131489
    [26] Y. Luo, Z. Wang, A multilevel Monte Carlo ensemble scheme for solving random parabolic PDEs, SIAM J. Sci. Comput., 41 (2019), A622–A642. https://doi.org/10.1137/18M1174635 doi: 10.1137/18M1174635
    [27] J. Martínez-Frutos, M. Kessler, A. Münch, F. Periago, Robust optimal Robin boundary control for the transient heat equation with random input data, Int. J. Numer. Meth. Eng., 108 (2016), 116–135. https://doi.org/10.1002/nme.5210 doi: 10.1002/nme.5210
    [28] J. Martínez-Frutos, F. P. Esparza, Optimal control of PDEs under uncertainty: An introduction with application to optimal shape design of structures, Cham: Springer, 2018. https://doi.org/10.1007/978-3-319-98210-6
    [29] L. Mathelin, M. Y. Hussaini, T. A. Zang, Stochastic approaches to uncertainty quantification in CFD simulations, Numer. Algor., 38 (2005), 209–236. https://doi.org/10.1007/BF02810624 doi: 10.1007/BF02810624
    [30] J. Meng, P. Y. Zhu, H. B. Li, A block GCROT $(m, k)$ method for linear systems with multiple right-hand sides, J. Comput. Appl. Math., 255 (2014), 544–554. https://doi.org/10.1016/j.cam.2013.06.014 doi: 10.1016/j.cam.2013.06.014
    [31] D. Xiu, J. S. Hesthaven, High-order collocation methods for differential equations with random inputs, SIAM J. Sci. Comput., 27 (2005), 1118–1139. https://doi.org/10.1137/040615201 doi: 10.1137/040615201
    [32] K. Ma, T. Sun, A non-overlapping DDM for optimal boundary control problems governed by parabolic equations, Appl. Math. Optim., 79 (2019), 769–795. https://doi.org/10.1007/s00245-017-9456-7 doi: 10.1007/s00245-017-9456-7
    [33] A. L. Teckentrup, P. Jantsch, C. G. Webster, M. Gunzburger, A multilevel stochastic collocation method for partial differential equations with random input data, SIAM/ASA J. Uncertain., 3 (2015), 1046–1074. https://doi.org/10.1137/140969002 doi: 10.1137/140969002
    [34] T. Yao, C. Ye, X. Luo, S. Xiang, An ensemble scheme for the numerical solution of a random transient heat equation with uncertain inputs, Numer. Algor., 94 (2023), 643–668. https://doi.org/10.1007/s11075-023-01514-z doi: 10.1007/s11075-023-01514-z
    [35] T. Yao, C. Ye, X. Luo, S. Xiang, A variational MAX ensemble numerical algorism for a transient heat model with random inputs, Netw. Heterog. Media, 19 (2024), 1013–1037. https://doi.org/10.3934/nhm.2024045 doi: 10.3934/nhm.2024045
    [36] C. Ye, T. Yao, H. Bi, X. Luo, A variational Crank-Nicolson ensemble Monte Carlo algorithm for a heat equation under uncertainty, J. Comput. Appl. Math., 451 (2024), 116068. https://doi.org/10.1016/j.cam.2024.116068 doi: 10.1016/j.cam.2024.116068
    [37] J. Yong, C. Ye, X. Luo, S. Sun, Improved error estimates of ensemble Monte Carlo methods for random transient heat equations with uncertain inputs, Comput. Appl. Math., 44 (2025), 58. https://doi.org/10.1007/s40314-024-03022-9 doi: 10.1007/s40314-024-03022-9
    [38] X. Zhu, E. M. Linebarger, D. Xiu, Multi-fidelity stochastic collocation method for computation of statistical moments, J. Comput. Phys., 341 (2017), 386–396. https://doi.org/10.1016/j.jcp.2017.04.022 doi: 10.1016/j.jcp.2017.04.022
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