CrayLabs/SmartSim-Zoo
A repository of CrayLabs and user contributed examples of using SmartSim.
This collection provides practical examples for integrating machine learning models directly into high-performance simulations without saving intermediate data to disk. It demonstrates how scientists and engineers can couple complex simulation workflows with real-time AI inference. Researchers in fields like molecular dynamics, climate modeling, and computational fluid dynamics would find this useful for accelerating their research.
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Use this if you are running complex scientific simulations and want to incorporate machine learning for online analysis or parameterization without the performance overhead of disk I/O.
Not ideal if you are looking for a standalone machine learning framework or if your simulations do not require real-time data exchange with AI models.
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Python
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Last pushed
Jun 11, 2024
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