A framework for building parallel, multi-disciplinary simulation software, focusing on modularity, extensibility, and high-performance computing.
Kratos Multiphysics is a framework for building parallel, multi-disciplinary simulation software. It provides a modular and extensible core written in C++ with a Python interface, enabling the development of custom simulation tools for engineering and scientific applications. The framework supports high-performance computing and includes applications for areas like fluid dynamics, structural mechanics, and particle simulations.
Researchers, engineers, and developers in computational mechanics, fluid dynamics, and structural analysis who need to create or extend simulation software for complex multi-physics problems.
Developers choose Kratos for its open-source BSD license, modular architecture that allows easy extension, and strong support for parallel computing, making it a versatile foundation for both academic and industrial simulation projects.
Kratos Multiphysics (A.K.A Kratos) is a framework for building parallel multi-disciplinary simulation software. Modularity, extensibility and HPC are the main objectives. Kratos has BSD license and is written in C++ with extensive Python interface.
Supports OpenMP and MPI, enabling scalable simulations across thousands of cores, as highlighted in the README for large-scale engineering problems.
Core system with pluggable applications for domains like DEM and Fluid Dynamics allows tailored extensions, fostering multi-disciplinary simulation development.
Extensive Python bindings to the C++ core facilitate scripting, integration, and rapid prototyping, as noted in the features for user accessibility.
Runs on Windows, Linux, and macOS, ensuring flexibility in deployment environments, as specified in the main features section.
BSD-4 license allows free commercial use, and modular architecture separates core from applications, promoting open-source collaboration and customization.
Compiling from source requires managing dependencies like Boost and Eigen via INSTALL.md, which can be time-consuming and error-prone for newcomers.
Documentation is split between GitHub, external wikis, and tutorials, potentially making it harder for users to find cohesive, up-to-date guidance.
Adding custom applications demands deep knowledge of C++ and the framework's architecture, limiting rapid experimentation for domain-specific needs.
Relies on external tools like GiD for pre- and post-processing, lacking a native graphical interface, which may hinder usability for non-programmers.
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