SmartFlow-AI4CFD/SmartFlow
CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
This framework helps researchers in computational fluid dynamics (CFD) develop and test advanced turbulence models, flow control strategies, and numerical algorithms. It takes outputs from traditional CFD simulations (like flow fields or pressures) and feeds them to deep reinforcement learning (DRL) models, which then generate control actions or model parameters to improve the simulation. It's designed for scientists and engineers working on complex fluid dynamics problems on high-performance computing platforms.
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Use this if you are a CFD researcher looking to integrate deep reinforcement learning with your existing Fortran or C++ CFD solvers to advance research in areas like turbulence modeling or flow control.
Not ideal if you are looking for a standalone CFD solver or a general-purpose machine learning library without a focus on high-performance scientific computing workflows.
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Language
Python
License
GPL-3.0
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Last pushed
Aug 01, 2025
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