TY - JOUR
T1 - MODIFIED TASE RUNGE-KUTTA METHODS FOR INTEGRATING STIFF DIFFERENTIAL EQUATIONS
AU - Aceto, Lidia
AU - Conte, Dajana
AU - Pagano, Giovanni
N1 - Publisher Copyright:
© 2025 Society for Industrial and Applied Mathematics.
PY - 2025
Y1 - 2025
N2 - We propose a new class of numerical methods, called MTRK for short, derived by an appropriate modification of the time-accurate and highly stable explicit (TASE) Runge-Kutta methods introduced by Bassenne, Fu, and Mani [J. Comput. Phys., 424 (2021), 109847] and then extended by Calvo, Montijano, and R\'andez [J. Comput. Phys., 436 (2021), 110316]. The MTRK methods are very efficient for dealing with the stiffness of differential problems without resorting to implicit methods, which incur high computational costs as they require the solution of nonlinear algebraic equations at each step. An in-depth analysis of the stability and consistency properties of MTRK methods via Butcher trees shows not only that they definitely improve existing TASE Runge-Kutta methods, but also that they can be advantageous compared to some well-known methods such as W-methods and Rosenbrock methods. This is confirmed by numerical experiments performed with nonlinear partial differential equations from applications.
AB - We propose a new class of numerical methods, called MTRK for short, derived by an appropriate modification of the time-accurate and highly stable explicit (TASE) Runge-Kutta methods introduced by Bassenne, Fu, and Mani [J. Comput. Phys., 424 (2021), 109847] and then extended by Calvo, Montijano, and R\'andez [J. Comput. Phys., 436 (2021), 110316]. The MTRK methods are very efficient for dealing with the stiffness of differential problems without resorting to implicit methods, which incur high computational costs as they require the solution of nonlinear algebraic equations at each step. An in-depth analysis of the stability and consistency properties of MTRK methods via Butcher trees shows not only that they definitely improve existing TASE Runge-Kutta methods, but also that they can be advantageous compared to some well-known methods such as W-methods and Rosenbrock methods. This is confirmed by numerical experiments performed with nonlinear partial differential equations from applications.
KW - Runge-Kutta methods
KW - TASE preconditioners
KW - W-methods
KW - numerical methods for PDEs
KW - stiff problems
UR - http://www.scopus.com/inward/record.url?scp=105004769346&partnerID=8YFLogxK
U2 - 10.1137/24M1667336
DO - 10.1137/24M1667336
M3 - Article
SN - 1064-8275
VL - 47
SP - A1652-A1680
JO - SIAM Journal of Scientific Computing
JF - SIAM Journal of Scientific Computing
IS - 3
ER -