Solving inverse problems using conditional invertible neural networks. JCP ArXiv
Govinda Anantha Padmanabha, Nicholas Zabaras
PyTorch Implementation of Solving inverse problems using conditional invertible neural networks.
- Rather than developing a surrogate for a forward model, we are training directly an inverse surrogate mapping output information of a physical system to an unknown input distributed parameter.
- A generative model based on conditional invertible neural networks (cINN) is developed.
- The cINN is trained to serve as an inverse surrogate model of physical systems governed by PDEs.
- The inverse surrogate model is used for the solution of inverse problems with unknown spatially-dependent parameters.
- The developed method is applied for the estimation of a non-Gaussian permeability field in multiphase flows using limited pressure and saturation data.
Mapping: observations → input space
PyTorch 1.0.0
Python 3
H5py
Matplotlib
Numpy
If you find this GitHub repository useful for your work, please consider to cite this work:
@article{padmanabha2021solving,
title={Solving inverse problems using conditional invertible neural networks},
journal={Journal of Computational Physics},
pages={110194},
year={2021},
publisher={Elsevier}
doi = {https://doi.org/10.1016/j.jcp.2021.110194 },
url = {https://www.sciencedirect.com/science/article/pii/S0021999121000899},
author = {Govinda Anantha Padmanabha and Nicholas Zabaras}
}