Data and Jupyter notebooks to reproduce the results of:
Edeling, W. (2023). On the deep active-subspace method. SIAM/ASA Journal on Uncertainty Quantification, 11(1), 62-90.
We applied the deep-active subspace method to:
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An HIV model consisting of 7 coupled ordinary differential equations, with 27 uncertain input parameters.
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A COVID19 model with 51 inputs parameters.
See the paper above for more information.
To reproduce the results of the HIV model, the following Jupyter notebook are present:
-
HIV/HIV.ipynb
: reproduce the results of the scalar quantities of interest. -
HIV/HIV_vector.ipynb
: reproduce the results of the vector-values quantity of interest.
To reproduce the results for the COVID19 model, run
COVID19/COVID19.ipynb
All required training data is also present in the HIV
and COVID19
directories, see the notebooks for a description.
This research is funded by the European Union Horizon 2020 research and innovation programme under grant agreement #800925 (VECMA project).