francocerino/scikit-reducedmodel
Reduced Order Models in a scikit-learn approach.
This tool helps scientists and engineers quickly generate accurate simulations of complex physical systems. You input a collection of high-fidelity simulation results along with the parameters used to create them. The tool then outputs a 'surrogate model' that can rapidly produce new simulation results for different parameters, drastically reducing computation time. This is for researchers or engineers who deal with computationally intensive simulations, such as those in general relativity or fluid dynamics.
No commits in the last 6 months. Available on PyPI.
Use this if you need to perform many simulations or parameter estimations for complex systems, but current methods are too slow, taking days or months on supercomputers.
Not ideal if your simulations are already fast enough for your needs or if your solutions don't exhibit redundancy across the parameter space.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Sep 11, 2025
Commits (30d)
0
Dependencies
7
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