This is an implementation of the bi-fidelity weighted learning for neural networks from the following paper:
De, S., Britton, J., Reynolds, M., Skinner, R., Jansen, K., & Doostan, A. (2020).
"On transfer learning of neural networks using bi-fidelity data for uncertainty propagation."
International Journal for Uncertainty Quantification, 10(6).
Link: http://www.dl.begellhouse.com/journals/52034eb04b657aea,3673619972b2eee6,3364eea04170ec7c.html
If you find the code useful please cite the paper using the following BibTex entry:
@article{de2020transfer,
title={On transfer learning of neural networks using bi-fidelity data for uncertainty propagation},
author={De, Subhayan and Britton, Jolene and Reynolds, Matthew and Skinner, Ryan and Jansen, Kenneth and Doostan, Alireza},
journal={International Journal for Uncertainty Quantification},
volume={10},
number={6},
year={2020},
publisher={Begel House Inc.}
}
To report any bugs, please use the email: Subhayan.De@colorado.edu
Website: www.subhayande.com
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.