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High-Order Operator-Splitting Methods for the Bidomain and Monodomain Models

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Mathematical and Numerical Modeling of the Cardiovascular System and Applications

Part of the book series: SEMA SIMAI Springer Series ((SEMA SIMAI,volume 16))

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Abstract

The bidomain and monodomain models are among the most widely used mathematical models to describe cardiac electrophysiology. They take the form of multi-scale reaction-diffusion partial differential equations that couple the dynamic behaviour on the cellular scale with that on the tissue scale. The systems of differential equations associated with these models are large and strongly non-linear, but they also have a distinct structure due to their multi-scale nature. For these reasons, numerical solutions to these systems are often found via operator-splitting methods. In this chapter, we provide a survey of operator-splitting methods for the numerical solution of differential equations. In particular, we focus on splitting methods with order higher than two that, according to the Sheng–Suzuki theorem, require backward time integration and historically have been considered unstable for solving deterministic parabolic systems. We demonstrate the stability of operator-splitting methods of up to order four to solve the bidomain and monodomain models on several examples arising in the field of cardiovascular modeling.

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References

  1. Ascher, U.M., Ruuth, S.J., Spiteri, R.J.: Implicit-explicit Runge–Kutta methods for time-dependent partial differential equations. Appl. Numer. Math. 25(2–3), 151–167 (1997)

    Article  MathSciNet  Google Scholar 

  2. Autumn, A.C., et al.: An overview of CellML 1.1, a biological model description language. Simulation 79(12), 740–747 (2003)

    Article  Google Scholar 

  3. Auzinger, W., Herfort, W.: Local error structures and order conditions in terms of lie elements for exponential splitting schemes. Opuscula Mathematica 34(2) (2014)

    Article  MathSciNet  Google Scholar 

  4. Auzinger, W., Herfort, W., Hofstätter, H., Koch, O.: Setup of order conditions for splitting methods. In: Computer Algebra in Scientific Computing: 18th International Workshop, CASC 2016, Bucharest, Romania, September 19–23, 2016, Proceedings, pp. 30–42. Springer International Publishing (2016)

    Google Scholar 

  5. Auzinger, W., Hofstätter, H., Ketcheson, D., Koch, O.: Practical splitting methods for the adaptive integration of nonlinear evolution equations. part i: Construction of optimized schemes and pairs of schemes. BIT Numerical Mathematics 1–20 (2016)

    Google Scholar 

  6. Blanes, S., Casas, F.: On the necessity of negative coefficients for operator splitting schemes of order higher than two. Appl. Numer. Math. 54(1), 23–37 (2005)

    Article  MathSciNet  Google Scholar 

  7. Blanes, S., Casas, F., Farres, A., Laskar, J., Makazaga, J., Murua, A.: New families of symplectic splitting methods for numerical integration in dynamical astronomy. Appl. Numer. Math. 68, 58–72 (2013)

    Article  MathSciNet  Google Scholar 

  8. Cherry, E.M., Greenside, H.S., Henriquez, C.S.: A space-time adaptive method for simulating complex cardiac dynamics. Phys. Rev. Lett. 84(6), 1343 (2000)

    Article  Google Scholar 

  9. Crouzeix, M.: Sur lapproximation des équations différentielles opérationelles linéaires par des méthodes de Runge–Kutta. PhD thesis, Université Paris (1978)

    Google Scholar 

  10. FitzHugh, R.: Mathematical models of excitation and propagation in nerve. In: Schwan, H.P. (ed.) Biological Engineering, pp. 1–85. McGraw-Hill (1966)

    Google Scholar 

  11. Geraldo-Giorda, L.: Nonlinear Dynamics in Biological Systems, vol. 7. Springer International Publishing (2016)

    Google Scholar 

  12. Godunov, S.K.: A difference method for numerical calculation of discontinuous solutions of the equations of hydrodynamics. Matematicheskii Sbornik 89(3), 271–306 (1959)

    MathSciNet  MATH  Google Scholar 

  13. Goldman, G., Kaper, T.J.: Nth-order operator splitting schemes and nonreversible systems. SIAM J. Numer. Anal. 33(1), 349–367 (1996)

    Article  MathSciNet  Google Scholar 

  14. Koch, O., Neuhauser, Ch., Thalhammer, M.: Embedded exponential operator splitting methods for the time integration of nonlinear evolution equations. Appl. Numer. Math. 63, 14–24 (2013)

    Article  MathSciNet  Google Scholar 

  15. LeVeque, R.J.: Numerical Methods for Conservation Laws. Birkhauser, Basel (1992)

    Book  Google Scholar 

  16. Luo, C.H., Rudy, Y.: A model of the ventricular cardiac action potential. depolarization, repolarization, and their interaction. Circulation Research 68(6), 1501–1526 (1991)

    Article  Google Scholar 

  17. Marchuk, G.I.: Splitting Methods. Nauka, Moscow (1988)

    Google Scholar 

  18. Marsh, M.E., Ziaratgahi, S.T., Spiteri, R.J.: The secrets to the success of the rush–larsen method and its generalizations. IEEE Trans. Biomed. Eng. 59(9), 2506–2515 (2012)

    Article  Google Scholar 

  19. Mirams, G.R., et al.: Chaste: An open source C++ library for computational physiology and biology. PLoS Comput. Biol. 9(3) (2013)

    Article  MathSciNet  Google Scholar 

  20. Niederer, S.A., et al.: Verification of cardiac tissue electrophysiology simulators using an n-version benchmark. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 369(1954), 4331–4351 (2011)

    Article  MathSciNet  Google Scholar 

  21. Nørsett, S.P.: Semi explicit Runge–Kutta methods. Mathematics and Computation, no. 6. University of Trondheim (1974)

    Google Scholar 

  22. Pathmanathan, P., Mirams, G.R., Southern, J., Whiteley, J.P.: The significant effect of the choice of ionic current integration method in cardiac electro-physiological simulations. Int. J. Numer. Methods Biomed. Eng. 27(11), 1751–1770 (2011)

    Article  MathSciNet  Google Scholar 

  23. Qu, Z., Garfinkel, A.: An advanced algorithm for solving partial differential equation in cardiac conduction. IEEE Trans. Biomed. Eng. 46(9), 1166–1168 (1999)

    Article  Google Scholar 

  24. Ruth, R.D.: A canonical integration technique. IEEE Trans. Nucl. Sci. 30 (1983)

    Article  Google Scholar 

  25. Sheng, Q.: Solving linear partial differential equations by exponential splitting. IMA J. Numer. Anal. 9, 199–212 (1989)

    Article  MathSciNet  Google Scholar 

  26. Sornborger, A.T.: Higher-order operator splitting methods for deterministic parabolic equations. Int. J. Comput. Math. 84(6), 887–893 (2007)

    Article  MathSciNet  Google Scholar 

  27. Sornborger, A.T., Stewart, E.D.: Higher-order methods for simulations on quantum computers. Phys. Rev. A 60(3), 765–789 (1999)

    Article  Google Scholar 

  28. Spiteri, R.J., Dean, R.C.: Stiffness analysis of cardiac electrophysiological models. Ann. Biomed. Eng. 38(12), 3592–3604 (2010)

    Article  Google Scholar 

  29. Strang, G.: On the construction and comparison of difference schemes. SIAM J. Numer. Anal. 5(3), 506–517 (1968)

    Article  MathSciNet  Google Scholar 

  30. Sundnes, J., Lines, G.T., Cai, X.: Computing the Electrical Activity in the Heart. Springer (2006)

    Google Scholar 

  31. Sundnes, J., Lines, G.T., Tveito, A.: An operator splitting method for solving the bidomain equations coupled to a volume conductor model for the torso. Mathematical Biosciences 194(2), 233–248 (2005)

    Article  MathSciNet  Google Scholar 

  32. Suzuki, M.: Solving linear partial differential equa tions by exponential splitting. IMA J. Numer. Anal. 9, 400–407 (1991)

    Google Scholar 

  33. Ten Tusscher, K.H.W.J., Panfilov, A.V.: Alternans and spiral breakup in a human ventricular tissue model. Am. J. Physiol. Heart Circ. Physiol. 291(3), H1088–H1100 (2006)

    Article  Google Scholar 

  34. Trotter, H.F.: On the product of semi-groups of operators. Proc. Am. Math. Soc. 10(4), 545–551 (1959)

    Article  MathSciNet  Google Scholar 

  35. Tung, L.: A bi-domain model for describing ischemic myocardial d-c potentials. PhD thesis, Massachusetts Institute of Technology (1978)

    Google Scholar 

  36. Yoshida, H.: Construction of higher order symplectic integrators. Phys. Lett. A 150(5), 262–268 (1990)

    Article  MathSciNet  Google Scholar 

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Cervi, J., Spiteri, R.J. (2018). High-Order Operator-Splitting Methods for the Bidomain and Monodomain Models. In: Boffi, D., Pavarino, L., Rozza, G., Scacchi, S., Vergara, C. (eds) Mathematical and Numerical Modeling of the Cardiovascular System and Applications. SEMA SIMAI Springer Series, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-96649-6_2

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