Feel++ is a comprehensive C++ library for Galerkin methods, such as finite and spectral element methods, used in the numerical solution of partial differential equations (PDEs) across one, two, and three dimensions. It is comprised of three main components:
Feel++ Core: This is the mathematical kernel of Feel++, offering versatile solutions to problems with different techniques for testing and comparing methods, such as continuous Galerkin (cG) and discontinuous Galerkin (dG) methods. It includes a wide range of numerical methods, closely follows mathematical abstractions of PDEs, supports high-performance computing scaling, and facilitates the creation of complex, typically non-linear, multi-physics applications in various fields including industry and healthcare.
Feel++ Toolboxes: These provide a suite of mono and multi-physics applications ready to address problems in fluid mechanics, solid mechanics, heat transfer (including conjugate heat transfer), fluid-structure interaction, electro and magnetostatics, thermoelectrics, and level set and multifluid dynamics.
Feel++ Model Order Reduction (MOR): This component offers tools and examples for working with reduced order models, which are crucial for simplifying complex models while retaining essential features.
Moreover, Feel++ provides Python interfaces for each of its components, allowing for manipulation of mathematical objects and models in Python, making it accessible for a wider range of users and applications.
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Summary
Feel++ is a comprehensive C++ library for Galerkin methods, such as finite and spectral element methods, used in the numerical solution of partial differential equations (PDEs) across one, two, and three dimensions. It is comprised of three main components:
Feel++ Core: This is the mathematical kernel of Feel++, offering versatile solutions to problems with different techniques for testing and comparing methods, such as continuous Galerkin (cG) and discontinuous Galerkin (dG) methods. It includes a wide range of numerical methods, closely follows mathematical abstractions of PDEs, supports high-performance computing scaling, and facilitates the creation of complex, typically non-linear, multi-physics applications in various fields including industry and healthcare.
Feel++ Toolboxes: These provide a suite of mono and multi-physics applications ready to address problems in fluid mechanics, solid mechanics, heat transfer (including conjugate heat transfer), fluid-structure interaction, electro and magnetostatics, thermoelectrics, and level set and multifluid dynamics.
Feel++ Model Order Reduction (MOR): This component offers tools and examples for working with reduced order models, which are crucial for simplifying complex models while retaining essential features.
Moreover, Feel++ provides Python interfaces for each of its components, allowing for manipulation of mathematical objects and models in Python, making it accessible for a wider range of users and applications.
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Description
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Additional information
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General information