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Numerical Methods for Delay Differential Equations

Author

Listed:
  • Bellen, Alfredo

    (Dipartimento di Matematica e Informatica, Universita' di Trieste, Italy)

  • Zennaro, Marino

    (Dipartimento di Matematica e Geoscienze, Universita di Trieste, Italy)

Abstract

The main purpose of the book is to introduce the readers to the numerical integration of the Cauchy problem for delay differential equations (DDEs). Peculiarities and differences that DDEs exhibit with respect to ordinary differential equations are preliminarily outlined by numerous examples illustrating some unexpected, and often surprising, behaviours of the analytical and numerical solutions. The effect of various kinds of delays on the regularity of the solution is described and some essential existence and uniqueness results are reported. The book is centered on the use of Runge-Kutta methods continuously extended by polynomial interpolation, includes a brief review of the various approaches existing in the literature, and develops an exhaustive error and well-posedness analysis for the general classes of one-step and multistep methods. The book presents a comprehensive development of continuous extensions of Runge-Kutta methods which are of interest also in the numerical treatment of more general problems such as dense output, discontinuous equations, etc. Some deeper insight into convergence and superconvergence of continuous Runge-Kutta methods is carried out for DDEs with various kinds of delays. The stepsize control mechanism is also developed on a firm mathematical basis relying on the discrete and continuous local error estimates. Classical results and a unconventional analysis of "stability with respect to forcing term" is reviewed for ordinary differential equations in view of the subsequent numerical stability analysis. Moreover, an exhaustive description of stability domains for some test DDEs is carried out and the corresponding stability requirements for the numerical methods are assessed and investigated. Alternative approaches, based on suitable formulation of DDEs as partial differential equations and subsequent semidiscretization are briefly described and compared with the classical approach. A list of available codes is provided, and illustrative examples, pseudo-codes and numerical experiments are included throughout the book.

Suggested Citation

  • Bellen, Alfredo & Zennaro, Marino, 2013. "Numerical Methods for Delay Differential Equations," OUP Catalogue, Oxford University Press, number 9780199671373.
  • Handle: RePEc:oxp:obooks:9780199671373
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    Cited by:

    1. Dubey, Balram & Sajan, & Kumar, Ankit, 2021. "Stability switching and chaos in a multiple delayed prey–predator model with fear effect and anti-predator behavior," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 164-192.
    2. Omran, A.K. & Zaky, M.A. & Hendy, A.S. & Pimenov, V.G., 2022. "An easy to implement linearized numerical scheme for fractional reaction–diffusion equations with a prehistorical nonlinear source function," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 218-239.
    3. Martina BOBALOVA & Veronika NOVOTNA, 2021. "Modeling Of Time Delayed Processes In Business Economics," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 15(1), pages 79-89, November.
    4. Dipty Sharma & Paramjeet Singh, 2020. "Discontinuous Galerkin approximation for excitatory-inhibitory networks with delay and refractory periods," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(03), pages 1-25, January.
    5. Banks, H.T. & Banks, J.E. & Bommarco, Riccardo & Laubmeier, A.N. & Myers, N.J. & Rundlöf, Maj & Tillman, Kristen, 2017. "Modeling bumble bee population dynamics with delay differential equations," Ecological Modelling, Elsevier, vol. 351(C), pages 14-23.
    6. Vsevolod G. Sorokin & Andrei V. Vyazmin, 2022. "Nonlinear Reaction–Diffusion Equations with Delay: Partial Survey, Exact Solutions, Test Problems, and Numerical Integration," Mathematics, MDPI, vol. 10(11), pages 1-39, May.
    7. Ahmed A. Mahmoud & Sarat C. Dass & Mohana S. Muthuvalu & Vijanth S. Asirvadam, 2017. "Maximum Likelihood Inference for Univariate Delay Differential Equation Models with Multiple Delays," Complexity, Hindawi, vol. 2017, pages 1-14, October.
    8. Benito Chen-Charpentier, 2021. "Stochastic Modeling of Plant Virus Propagation with Biological Control," Mathematics, MDPI, vol. 9(5), pages 1-16, February.
    9. Fernando Alcántara-López & Carlos Fuentes & Carlos Chávez & Jesús López-Estrada & Fernando Brambila-Paz, 2022. "Fractional Growth Model with Delay for Recurrent Outbreaks Applied to COVID-19 Data," Mathematics, MDPI, vol. 10(5), pages 1-18, March.
    10. Jian, Huan-Yan & Huang, Ting-Zhu & Ostermann, Alexander & Gu, Xian-Ming & Zhao, Yong-Liang, 2021. "Fast numerical schemes for nonlinear space-fractional multidelay reaction-diffusion equations by implicit integration factor methods," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    11. Benito Chen-Charpentier, 2023. "Delays and Exposed Populations in Infection Models," Mathematics, MDPI, vol. 11(8), pages 1-22, April.
    12. Arnott, Richard & Buli, Joshua, 2018. "Solving for equilibrium in the basic bathtub model," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 150-175.
    13. Mahmoudi, Fatemeh & Tahmasebi, Mahdieh, 2022. "The convergence of a numerical scheme for additive fractional stochastic delay equations with H>12," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 219-231.

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