Data generation and processing pipeline as well as deep learning utilities to train neural networks to predict tortuosity of porous media
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This project used convolutional neural networks to predict the steady-state concentration of 3D porous media, and subsequently calculates the tortuosity. This package includes data generation, processing, training, and post-processing functions. The loss function includes a Laplacian loss, which is a physics-informed loss.
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samly97/soil-net
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This project used convolutional neural networks to predict the steady-state concentration of 3D porous media, and subsequently calculates the tortuosity. This package includes data generation, processing, training, and post-processing functions. The loss function includes a Laplacian loss, which is a physics-informed loss.
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