Componentwise Block Partitioning is a new strategy to solve stiff ODEs, based on Block Backward Differentiation Formulas (BBDFs), and block of Adam type formulas. In this partitioning technique, the ODEs system is initially solved by Adam formulas until the equation that cause instability and stiffness is identified. Then, the equations that caused instability are placed into stiff subsystem and solved using BBDF. Numerical comparisons with code in the literature such as ode15s show the efficiency of the proposed partitioning technique.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Componentwise block partitioning: A new strategy to solve stiff ordinary differential equations


    Beteiligte:
    Othman, K. I. (Autor:in) / Ibrahim, Z. B. (Autor:in) / Sivasundaram, Seenith (Herausgeber:in)

    Kongress:

    9TH INTERNATIONAL CONFERENCE ON MATHEMATICAL PROBLEMS IN ENGINEERING, AEROSPACE AND SCIENCES: ICNPAA 2012 ; 2012 ; Vienna, Austria


    Erschienen in:

    AIP Conference Proceedings ; 1493 , 1 ; 724-726


    Erscheinungsdatum :

    06.11.2012


    Format / Umfang :

    3 pages





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Componentwise block partitioning: A new strategy to solve stiff ordinary differential equations

    Othman, K.I. / Ibrahim, Z.B. | British Library Conference Proceedings | 2012



    A parameter estimation method for stiff ordinary differential equations using particle swarm optimisation

    Arloff, William / Schmitt, Karl R.B. / Venstrom, Luke J. | British Library Online Contents | 2018


    The ATOMFT integrator - Using Taylor series to solve ordinary differential equations

    BERRYMAN, KENNETH / STANFORD, RICHARD / BRECKHEIMER, PETER | AIAA | 1988


    An Efficient Direct Diagonal Hybrid Block Method for Stiff Second Order Differential Equations

    Rasid, Norshakila Abd / Ibrahim, Zarina Bibi / Majid, Zanariah Abdul et al. | Springer Verlag | 2022