/deep_active_subspace_data

Data and scripts to reproduce the results of the "On the deep active subspace method" article.

Primary LanguageJupyter NotebookMIT LicenseMIT

Deep active subspace data

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:

  • An HIV model consisting of 7 coupled ordinary differential equations, with 27 uncertain input parameters.

  • A COVID19 model with 51 inputs parameters.

See the paper above for more information.

Contents

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.

Funding

This research is funded by the European Union Horizon 2020 research and innovation programme under grant agreement #800925 (VECMA project).