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ExaHyPE : an engine for parallel dynamically adaptive simulations of wave problems.

Reinarz, Anne and Charrier, Dominic E. and Bader, Michael and Bovard, Luke and Dumbser, Michael and Duru, Kenneth and Fambri, Francesco and Gabriel, Alice-Agnes and Gallard, Jean-Matthieu and Köppel, Sven and Krenz, Lukas and Rannabauer, Leonhard and Rezzolla, Luciano and Samfass, Philipp and Tavelli, Maurizio and Weinzierl, Tobias (2020) 'ExaHyPE : an engine for parallel dynamically adaptive simulations of wave problems.', Computer physics communications., 254 . p. 107251.

Abstract

ExaHyPE (“An Exascale Hyperbolic PDE Engine”) is a software engine for solving systems of first-order hyperbolic partial differential equations (PDEs). Hyperbolic PDEs are typically derived from the conservation laws of physics and are useful in a wide range of application areas. Applications powered by ExaHyPE can be run on a student’s laptop, but are also able to exploit thousands of processor cores on state-of-the-art supercomputers. The engine is able to dynamically increase the accuracy of the simulation using adaptive mesh refinement where required. Due to the robustness and shock capturing abilities of ExaHyPE’s numerical methods, users of the engine can simulate linear and non-linear hyperbolic PDEs with very high accuracy. Users can tailor the engine to their particular PDE by specifying evolved quantities, fluxes, and source terms. A complete simulation code for a new hyperbolic PDE can often be realised within a few hours — a task that, traditionally, can take weeks, months, often years for researchers starting from scratch. In this paper, we showcase ExaHyPE’s workflow and capabilities through real-world scenarios from our two main application areas: seismology and astrophysics.

Item Type:Article
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.cpc.2020.107251
Publisher statement:© 2020 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Date accepted:25 February 2020
Date deposited:03 March 2020
Date of first online publication:03 March 2020
Date first made open access:16 June 2020

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