Weishuo93/NN_Pred
An interfacing library to deploy machine-learning models in CFD codes easily
This library helps computational fluid dynamics (CFD) engineers and researchers use machine learning models directly within their existing C++, Fortran, OpenFOAM, or CFL3D simulations. It allows you to integrate trained neural network models (from TensorFlow or ONNX formats) into your CFD code, taking simulation data as input and outputting model predictions, which can then be used to enhance or accelerate your simulations. This is primarily for engineers and scientists working with CFD software who want to leverage AI/ML without rewriting their core simulation code.
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Use this if you are a CFD engineer or researcher who wants to deploy pre-trained machine learning models directly into your C++, Fortran, OpenFOAM, or CFL3D simulation code to improve or augment your fluid dynamics computations.
Not ideal if you are looking for a general-purpose machine learning library for Python or other high-level languages, or if your work does not involve computational fluid dynamics.
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Language
C++
License
MIT
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Last pushed
Jan 15, 2025
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