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CAMWA special issue on open-source numerical solver

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    Fei Xu
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    Dear OpenLB community,

    Computers & Mathematics with Applications (Elsevier, current impact factor 1.860) will launch a special issue dedicated to open-source numerical solvers for partial differential equations (PDEs) co-edited by Qingang Xiong (Senior Scientist, General Motors, USA), Vadym Aizinger (Senior Scientist, Alfred Wegener Institute for Polar and Marine Research, Germany), Fei Xu (R&D Engineer, Ansys Inc, USA), and Guillaume Ducrozet (Associate Professor, Ecole Centrale de Nantes, France).
    The primary purpose of this special issue is to provide an overview of the progress in this rapidly developing area and to identify current trends and near-term prospects in connection with the algorithm design, theoretical development, and various areas of application of open-source software for PDEs. Our goal is to let authors focus on the software design, algorithms, applications and future prospects of open source PDE solvers. Articles focusing on these topics are usually very difficult to publish in refereed journals in either applied mathematics, or engineering, or computer science. In addition, we attempt to facilitate better communication between the authors and the users of such packages by providing the developers with a forum to present their work and supplying an up-to-date list of open source PDE solvers.
    Major numerical methods covered in this special issues include, but not limited to, finite difference methods, finite element methods, finite volume methods, spectral methods, meshfree/meshless methods (e.g. LBM and SPH), gradient discretization methods, domain decomposition methods, time discretization methods, as well as multigrid methods (in conjunction with spatial discretization).
    Computers & Mathematics with Applications (Elsevier, current impact factor 1.860) will launch a special issue dedicated to open-source numerical solvers for partial differential equations (PDEs) co-edited by Qingang Xiong (Senior Scientist, General Motors, USA), Vadym Aizinger (Senior Scientist, Alfred Wegener Institute for Polar and Marine Research, Germany), Fei Xu (R&D Engineer, Ansys Inc, USA), and Guillaume Ducrozet (Associate Professor, Ecole Centrale de Nantes, France).
    The primary purpose of this special issue is to provide an overview of the progress in this rapidly developing area and to identify current trends and near-term prospects in connection with the algorithm design, theoretical development, and various areas of application of open-source software for PDEs. Our goal is to let authors focus on the software design, algorithms, applications and future prospects of open source PDE solvers. Articles focusing on these topics are usually very difficult to publish in refereed journals in either applied mathematics, or engineering, or computer science. In addition, we attempt to facilitate better communication between the authors and the users of such packages by providing the developers with a forum to present their work and supplying an up-to-date list of open source PDE solvers.
    Major numerical methods covered in this special issues include, but not limited to, finite difference methods, finite element methods, finite volume methods, spectral methods, meshfree/meshless methods (e.g. LBM and SPH), gradient discretization methods, domain decomposition methods, time discretization methods, as well as multigrid methods (in conjunction with spatial discretization).
    The guest editors of this special issue invite authors of open-source packages interested in having their package listed in the editorial as well as the potential contributors to the special issue to fill out by November 30th a short information sheet (https://goo.gl/forms/4LdrD3BCVGtMAZef1). Please note that, required by the journal, a printed published paper must contain at least 15 pages. The estimated manuscript submission deadline is June 30, 2019. The guest editors encourage you to help spreading this “Call for Papers” to your colleagues and collaborators active in this area.

    Thanks and best regards,
    Fei Xu

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