hao12312/caae-drdcn-inverse

Deep residual networks for dimensionality reduction and surrogate modeling in high-dimensional inverse problems

Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian conductivities

We have now uploaded the codes of CAAE. The codes of DRDCN will be uploaded soon.

Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian conductivities

Shaoxing Mo, Nicholas Zabaras, Xiaoqing Shi, Jichun Wu

Dependencies

• python 3
• PyTorch 0.4
• h5py
• matplotlib
• seaborn

Citation

See Mo et al. (2020) for more information. If you find this repo useful for your research, please consider to cite:

@article{doi:10.1029/2019WR026082,
author = {Mo, Shaoxing and Zabaras, Nicholas and Shi, Xiaoqing and Wu, Jichun},
title = {Integration of adversarial autoencoders with residual dense convolutional networks for estimation of 
non-Gaussian hydraulic conductivities},
journal = {Water Resources Research},
volume = {n/a},
number = {n/a},
pages = {e2019WR026082},
doi = {10.1029/2019WR026082},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019WR026082}
}

or:

Mo, S., Zabaras, N., Shi, X., & Wu, J. ( 2020). Integration of adversarial autoencoders with residual dense 
convolutional networks for estimation of non‐Gaussian hydraulic conductivities. Water Resources Research, 
56, e2019WR026082. https://doi.org/10.1029/2019WR026082

Questions

Contact Shaoxing Mo ([email protected]) or Nicholas Zabaras ([email protected]) with questions or comments.