Journal of Computational Physics
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
An explicit multi-time-stepping algorithm for aerodynamic flows
ICCAM '96 Proceedings of the seventh international congress on Computational and applied mathematics
Journal of Parallel and Distributed Computing - Special issue on dynamic load balancing
The Runge-Kutta discontinuous Galerkin method for conservation laws V multidimensional systems
Journal of Computational Physics
High Resolution Schemes for Conservation Laws with Locally Varying Time Steps
SIAM Journal on Scientific Computing
Aspects of discontinuous Galerkin methods for hyperbolic conservation laws
Finite Elements in Analysis and Design - Robert J. Melosh medal competition
A parallel explicit/implicit time stepping scheme on block-adaptive grids
Journal of Computational Physics
Application of implicit-explicit high order Runge-Kutta methods to discontinuous-Galerkin schemes
Journal of Computational Physics
Multirate Timestepping Methods for Hyperbolic Conservation Laws
Journal of Scientific Computing
Multirate Explicit Adams Methods for Time Integration of Conservation Laws
Journal of Scientific Computing
Local adaptive mesh refinement for shock hydrodynamics
Journal of Computational Physics
High-Order Local Time Stepping on Moving DG Spectral Element Meshes
Journal of Scientific Computing
Hi-index | 31.45 |
We develop an efficient local time-stepping algorithm for the method of lines approach to numerical solution of transient partial differential equations. The need for local time-stepping arises when adaptive mesh refinement results in a mesh containing cells of greatly different sizes. The global CFL number and, hence, the global time step, are defined by the smallest cell size. This can be inefficient as a few small cells may impose a restrictive time step on the whole mesh. A local time-stepping scheme allows us to use the local CFL number which reduces the total number of function evaluations. The algorithm is based on a second order Runge-Kutta time integration. Its important features are a small stencil and the second order accuracy in the L^2 and L^~ norms.