4 research outputs found
Collaborative research: ITR: global multi-scale kinetic simulations of the earth's magnetosphere using parallel discrete event simulation
Issued as final reportNational Science Foundation (U.S.
Schéma numérique asynchrone du second ordre pour la simulation des équations aux dérivées partielles linéaires
International audienceWe propose an asynchronous method for the explicit integration of multi-scale partial differential equations. This method is restricted by a local CFL (Courant Friedrichs Lewy) condition rather than the traditional global CFL condition. Moreover, contrary to other local time-stepping (LTS) methods, the asynchronous algorithm permits the selection of independent time steps in each mesh element. We derived an asynchronous Runge–Kutta 2 (ARK2) scheme from a standard explicit Runge–Kutta method and we proved that the ARK2 scheme is second order convergent. Comparing with the classical integration, the asynchronous scheme is effective in terms of computation time.Nous proposons une méthode asynchrone d'intégration explicite pour les équations aux dérivées partielles multi-échelles. Cette méthode est contrainte par une constante CFL locale au lieu de la constante CFL globale beaucoup plus restrictive. De plus et contrairement aux méthodes classiques de pas de temps local, l'algorithme asynchrone permet d'utiliser des pas de temps indépendants dans chaque élément du maillage. Nous développons un schéma asynchrone de type Runge–Kutta 2 (ARK2) à partir de la méthode Runge–Kutta explicite standard et montrons que le schéma ARK2 obtenu est convergent au second ordre. Comparativement aux méthodes classiques d'intégration synchrone, le schéma ARK2 est très efficaces en terme de temps de calcul
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CellLab-CTS 2015: continuous-time stochastic cellular automaton modeling using Landlab
CellLab-CTS 2015 is a Python-language software library for creating two-dimensional, continuous-time stochastic (CTS) cellular automaton models. The model domain consists of a set of grid nodes, with each node assigned an integer state code that represents its condition or composition. Adjacent pairs of nodes may undergo transitions to different states, according to a user-defined average transition rate. A model is created by writing a Python code that defines the possible states, the transitions, and the rates of those transitions. The code instantiates, initializes, and runs one of four object classes that represent different types of CTS models. CellLab-CTS provides the option of using either square or hexagonal grid cells. The software provides the ability to treat particular grid-node states as moving particles, and to track their position over time. Grid nodes may also be assigned user-defined properties, which the user can update after each transition through the use of a callback function. As a component of the Landlab modeling framework, CellLab-CTS models take advantage of a suite of Landlab's tools and capabilities, such as support for standardized input and output