katiana22/GDM-PCE
Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".
This tool helps scientists and engineers quickly understand the impact of uncertain input parameters on complex systems that produce a lot of data. You provide your high-dimensional simulation outputs and input parameters, and it generates a simplified, fast-running model. This model allows you to perform uncertainty quantification and Monte Carlo simulations much more efficiently.
No commits in the last 6 months.
Use this if you need to perform uncertainty quantification on complex models with high-dimensional outputs, especially when you have limited data and want to accelerate simulations.
Not ideal if your model outputs are not high-dimensional or if you need extremely fast model construction for very large hyperparameter spaces, as the decoder part can be time-consuming.
Stars
38
Forks
6
Language
Jupyter Notebook
License
MIT
Last pushed
Jun 14, 2022
Commits (30d)
0
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