Periodic and heterogeneous solid and velocity data used to train and validate CNN models
Data files
May 05, 2023 version files 1.92 GB
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45PPI_domain1_solid.mat
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45PPI_domain1_vfield.mat
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45PPI_domain2_solid.mat
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45PPI_domain2_vfield.mat
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65PPI_domain1_solid.mat
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65PPI_domain1_vfield.mat
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65PPI_domain2_solid.mat
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65PPI_domain2_vfield.mat
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80PPI_domain1_solid.mat
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80PPI_domain1_vfield.mat
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80PPI_domain2_solid.mat
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80PPI_domain2_vfield.mat
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BCC_domain1_solid.mat
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BCC_domain1_vfield.mat
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BCC_domain10_solid.mat
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BCC_domain10_vfield.mat
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BCC_domain2_solid.mat
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BCC_domain2_vfield.mat
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BCC_domain3_solid.mat
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BCC_domain3_vfield.mat
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BCC_domain4_solid.mat
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BCC_domain4_vfield.mat
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BCC_domain5_solid.mat
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BCC_domain5_vfield.mat
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BCC_domain6_solid.mat
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BCC_domain6_vfield.mat
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BCC_domain7_solid.mat
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BCC_domain7_vfield.mat
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BCC_domain8_solid.mat
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BCC_domain8_vfield.mat
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BCC_domain9_solid.mat
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BCC_domain9_vfield.mat
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CaF2_domain1_solid.mat
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CaF2_domain1_vfield.mat
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CaF2_domain10_solid.mat
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CaF2_domain10_vfield.mat
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CaF2_domain2_solid.mat
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CaF2_domain2_vfield.mat
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CaF2_domain3_solid.mat
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CaF2_domain3_vfield.mat
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CaF2_domain4_solid.mat
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CaF2_domain4_vfield.mat
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CaF2_domain5_solid.mat
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CaF2_domain5_vfield.mat
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CaF2_domain6_solid.mat
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CaF2_domain6_vfield.mat
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CaF2_domain7_solid.mat
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CaF2_domain7_vfield.mat
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CaF2_domain8_solid.mat
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CaF2_domain8_vfield.mat
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CaF2_domain9_solid.mat
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CaF2_domain9_vfield.mat
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CaTiO3_domain1_solid.mat
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CaTiO3_domain1_vfield.mat
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CaTiO3_domain10_solid.mat
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CaTiO3_domain10_vfield.mat
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CaTiO3_domain2_solid.mat
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CaTiO3_domain2_vfield.mat
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CaTiO3_domain3_solid.mat
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CaTiO3_domain3_vfield.mat
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CaTiO3_domain4_solid.mat
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CaTiO3_domain4_vfield.mat
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CaTiO3_domain5_solid.mat
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CaTiO3_domain5_vfield.mat
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CaTiO3_domain6_solid.mat
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CaTiO3_domain6_vfield.mat
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CaTiO3_domain7_solid.mat
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CaTiO3_domain7_vfield.mat
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CaTiO3_domain8_solid.mat
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CaTiO3_domain8_vfield.mat
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CaTiO3_domain9_solid.mat
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CaTiO3_domain9_vfield.mat
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CsCl_domain1_solid.mat
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CsCl_domain1_vfield.mat
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CsCl_domain10_solid.mat
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CsCl_domain10_vfield.mat
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CsCl_domain2_solid.mat
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CsCl_domain2_vfield.mat
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CsCl_domain3_solid.mat
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CsCl_domain3_vfield.mat
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CsCl_domain4_solid.mat
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CsCl_domain4_vfield.mat
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CsCl_domain5_solid.mat
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CsCl_domain5_vfield.mat
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CsCl_domain6_solid.mat
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CsCl_domain6_vfield.mat
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CsCl_domain7_solid.mat
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CsCl_domain7_vfield.mat
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CsCl_domain8_solid.mat
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CsCl_domain8_vfield.mat
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CsCl_domain9_solid.mat
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CsCl_domain9_vfield.mat
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FCC_domain1_solid.mat
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FCC_domain1_vfield.mat
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FCC_domain10_solid.mat
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FCC_domain10_vfield.mat
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FCC_domain2_solid.mat
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FCC_domain2_vfield.mat
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FCC_domain3_solid.mat
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FCC_domain3_vfield.mat
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FCC_domain4_solid.mat
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FCC_domain4_vfield.mat
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FCC_domain5_solid.mat
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FCC_domain5_vfield.mat
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FCC_domain6_solid.mat
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FCC_domain6_vfield.mat
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FCC_domain7_solid.mat
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FCC_domain7_vfield.mat
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FCC_domain8_solid.mat
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FCC_domain8_vfield.mat
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FCC_domain9_solid.mat
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FCC_domain9_vfield.mat
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NaCl_domain1_solid.mat
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NaCl_domain1_vfield.mat
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NaCl_domain10_solid.mat
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NaCl_domain10_vfield.mat
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NaCl_domain2_solid.mat
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NaCl_domain2_vfield.mat
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NaCl_domain3_solid.mat
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NaCl_domain3_vfield.mat
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NaCl_domain4_solid.mat
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NaCl_domain4_vfield.mat
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NaCl_domain5_solid.mat
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NaCl_domain5_vfield.mat
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NaCl_domain6_solid.mat
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NaCl_domain6_vfield.mat
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NaCl_domain7_solid.mat
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NaCl_domain7_vfield.mat
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NaCl_domain8_solid.mat
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NaCl_domain8_vfield.mat
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NaCl_domain9_solid.mat
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NaCl_domain9_vfield.mat
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PolySphere_domain1_solid.mat
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PolySphere_domain1_vfield.mat
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PolySphere_domain2_solid.mat
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PolySphere_domain2_vfield.mat
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PolySphere_domain3_solid.mat
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PolySphere_domain3_vfield.mat
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PolySphere_domain4_solid.mat
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PolySphere_domain4_vfield.mat
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PolySphere_domain5_solid.mat
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PolySphere_domain5_vfield.mat
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PolySphere_domain6_solid.mat
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PolySphere_domain6_vfield.mat
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PolySphere_domain7_solid.mat
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PolySphere_domain7_vfield.mat
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PolySphere_domain8_solid.mat
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PolySphere_domain8_vfield.mat
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PolySphere_domain9_solid.mat
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PolySphere_domain9_vfield.mat
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Porous_Media_Description.xlsx
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README.md
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SCC_domain1_solid.mat
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SCC_domain1_vfield.mat
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SCC_domain10_solid.mat
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SCC_domain10_vfield.mat
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SCC_domain2_solid.mat
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SCC_domain2_vfield.mat
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SCC_domain3_solid.mat
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SCC_domain3_vfield.mat
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SCC_domain4_solid.mat
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SCC_domain4_vfield.mat
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SCC_domain5_solid.mat
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SCC_domain5_vfield.mat
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SCC_domain6_solid.mat
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SCC_domain6_vfield.mat
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SCC_domain7_solid.mat
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SCC_domain7_vfield.mat
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SCC_domain8_solid.mat
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SCC_domain8_vfield.mat
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SCC_domain9_solid.mat
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SCC_domain9_vfield.mat
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TCC_domain1_solid.mat
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TCC_domain1_vfield.mat
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TCC_domain10_solid.mat
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TCC_domain10_vfield.mat
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TCC_domain2_solid.mat
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TCC_domain2_vfield.mat
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TCC_domain3_solid.mat
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TCC_domain3_vfield.mat
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TCC_domain4_solid.mat
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TCC_domain4_vfield.mat
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TCC_domain5_solid.mat
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TCC_domain5_vfield.mat
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TCC_domain6_solid.mat
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TCC_domain6_vfield.mat
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TCC_domain7_solid.mat
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TCC_domain7_vfield.mat
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TCC_domain8_solid.mat
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TCC_domain8_vfield.mat
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TCC_domain9_solid.mat
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TCC_domain9_vfield.mat
Abstract
Data-driven deep learning models are emerging as a promising method for characterizing pore-scale flow through complex porous media while requiring minimal computational power. However, previous models often require extensive computation to simulate flow through synthetic porous media for use as training data. We propose a convolutional neural network trained solely on periodic unit cells to predict pore-scale velocity fields of complex heterogeneous porous media from binary images without the need for further image processing. Our model is trained using a range of simple and complex unit cells that can be obtained analytically or numerically at a low computational cost. Our results show that the model accurately predicts the permeability and pore-scale flow characteristics of synthetic porous media and real reticulated foams. We significantly improve the convergence of numerical simulations by using the predictions from our model as initial guesses. Our approach addresses the limitations of previous models and improves computational efficiency, enabling the rigorous characterization of large batches of complex heterogeneous porous media for a variety of engineering applications.