GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
On Godunov-type methods near low densities
Journal of Computational Physics
SIAM Journal on Scientific and Statistical Computing
An explicit multi-time-stepping algorithm for aerodynamic flows
ICCAM '96 Proceedings of the seventh international congress on Computational and applied mathematics
The LASY preprocessor and its application to general multidimensional codes
Journal of Computational Physics
A solution-adaptive upwind scheme for ideal magnetohydrodynamics
Journal of Computational Physics
Adaptive blocks: a high performance data structure
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Hyperbolic divergence cleaning for the MHD equations
Journal of Computational Physics
Semirelativistic magnetohydrodynamics and physics-based convergence acceleration
Journal of Computational Physics
Divergence- and curl-preserving prolongation and restriction formulas
Journal of Computational Physics
Artificial wind: a new framework to construct simple and efficient upwind shock-capturing schemes
Journal of Computational Physics
Overture: An Object-Oriented Framework for Solving Partial Differential Equations
ISCOPE '97 Proceedings of the Scientific Computing in Object-Oriented Parallel Environments
The Architecture of the Earth System Modeling Framework
Computing in Science and Engineering
Solution-Adaptive Magnetohydrodynamics for Space Plasmas: Sun-to-Earth Simulations
Computing in Science and Engineering
A multi-state HLL approximate Riemann solver for ideal magnetohydrodynamics
Journal of Computational Physics
A parallel explicit/implicit time stepping scheme on block-adaptive grids
Journal of Computational Physics
Short note: A TVD principle and conservative TVD schemes for adaptive Cartesian grids
Journal of Computational Physics
Hall magnetohydrodynamics on block-adaptive grids
Journal of Computational Physics
Local adaptive mesh refinement for shock hydrodynamics
Journal of Computational Physics
Classical and semirelativistic magnetohydrodynamics with anisotropic ion pressure
Journal of Computational Physics
A stochastic approach to the solution of magnetohydrodynamic equations
Journal of Computational Physics
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
Hi-index | 31.46 |
Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different relevant physics in different domains. A multi-physics system can be modeled by a software framework comprising several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamic (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical schemes. Depending on the application, we find that different time stepping methods are optimal. Several of the time integration schemes exploit the block-based granularity of the grid structure. The framework and the adaptive algorithms enable physics-based space weather modeling and even short-term forecasting.