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    MFEM: A modular finite element methods library

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    Articlefile1.pdf
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    7.181Mb
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    PDF
    Description:
    Pre-print
    Embargo End Date:
    2022-07-11
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    Type
    Article
    Authors
    Anderson, Robert
    Andrej, Julian cc
    Barker, Andrew
    Bramwell, Jamie
    Camier, Jean Sylvain
    Cerveny, Jakub
    Dobrev, Veselin
    Dudouit, Yohann
    Fisher, Aaron
    Kolev, Tzanio cc
    Pazner, Will
    Stowell, Mark
    Tomov, Vladimir
    Akkerman, Ido
    Dahm, Johann
    Medina, David
    Zampini, Stefano cc
    KAUST Department
    Extreme Computing Research Center
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-07-11
    Online Publication Date
    2020-07-11
    Print Publication Date
    2020-07
    Embargo End Date
    2022-07-11
    Submitted Date
    2020-05-15
    Permanent link to this record
    http://hdl.handle.net/10754/664331
    
    Metadata
    Show full item record
    Abstract
    MFEM is an open-source, lightweight, flexible and scalable C++ library for modular finite element methods that features arbitrary high-order finite element meshes and spaces, support for a wide variety of discretization approaches and emphasis on usability, portability, and high-performance computing efficiency. MFEM's goal is to provide application scientists with access to cutting-edge algorithms for high-order finite element meshing, discretizations and linear solvers, while enabling researchers to quickly and easily develop and test new algorithms in very general, fully unstructured, high-order, parallel and GPU-accelerated settings. In this paper we describe the underlying algorithms and finite element abstractions provided by MFEM, discuss the software implementation, and illustrate various applications of the library.
    Citation
    Anderson, R., Andrej, J., Barker, A., Bramwell, J., Camier, J.-S., Cerveny, J., … Zampini, S. (2020). MFEM: A modular finite element methods library. Computers & Mathematics with Applications. doi:10.1016/j.camwa.2020.06.009
    Sponsors
    This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, LLNL-JRNL-795849.The MFEM project would not have been possible without the help and advice of Joachim Schöberl, Panayot Vassilevski, and all the contributors in the MFEM open-source community, see https://mfem.org/about and https://github.com/orgs/mfem/people. MFEM has been supported by a number of U.S. Department of Energy (DOE) grants, including the Applied Math and SciDAC programs in the DOE Office of Science, and the ASC and LDRD programs in NNSA. MFEM is also a major participant in the co-design Center for Efficient Exascale Discretizations (CEED) in the DOE's Exascale Computing Project. This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes.
    Publisher
    Elsevier BV
    Journal
    Computers and Mathematics with Applications
    DOI
    10.1016/j.camwa.2020.06.009
    arXiv
    1911.09220
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0898122120302583
    http://arxiv.org/pdf/1911.09220
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.camwa.2020.06.009
    Scopus Count
    Collections
    Articles; Extreme Computing Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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