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We present a SE(3)-equivariant graph neural network (GNN) approach that directly predicting the formation factor and effective permeability from micro-CT images.
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Permeability-porosity relationship: A reexamination of the kozeny-carman equation based on a fractal pore-space geometry assumption
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Predicting macroscopic transport properties using microscopic image data
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Calculating the effective permeability of sandstone with multiscale lattice boltzmann/finite element simulations
Joshua A White, Ronaldo I Borja, and Joanne T Fredrich · 2006
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A fft-based method to compute the permeability induced by a stokes slip flow through a porous medium
Vincent Monchiet, Guy Bonnet, and Guy Lauriat · 2009
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Pore-scale modeling of electrical and fluid transport in berea sandstone
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Jacob Bear · 2013
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A fourier based numerical method for computing the dynamic permeability of periodic porous media
Trung-Kien Nguyen, Vincent Monchiet, and Guy Bonnet · 2013
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On the relationship between induced polarization and surface conductivity: Implications for petrophysical interpretation of electrical measurements
Andreas Weller, Lee Slater, and Sven Nordsiek · 2013
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Modeling the hydro-mechanical responses of strip and circular punch loadings on water-saturated collapsible geomaterials
WaiChing Sun, Qiushi Chen, and Jakob T Ostien · 2014
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An fft-based galerkin method for homogenization of periodic media
Jaroslav Vondřejc, Jan Zeman, and Ivo Marek · 2014
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Stress-induced anisotropy in granular materials: fabric, stiffness, and permeability
Matthew R Kuhn, WaiChing Sun, and Qi Wang · 2015
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Efficient map reconstruction and augmentation via topological methods
S. Wang, Y. Wang, and Y. Li · 2015
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A machine learning-based design representation method for designing heterogeneous microstructures
Hongyi Xu, Ruoqian Liu, Alok Choudhary, and Wei Chen · 2015
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Deep-learning methods for predicting permeability from 2d/3d binary-segmented images
Nattavadee Srisutthiyakorn* · 2016
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Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability
Joost H van der Linden, Guillermo A Narsilio, and Antoinette Tordesillas · 2016
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Improved road network reconstruction using discrete morse theory
Tamal K. Dey, Jiayuan Wang, and Yusu Wang · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation
George Kim, Haoyan Diao, Chanho Lee, AT Samaei, Tu Phan, Maarten de Jong, Ke An, Dong Ma, Peter K Liaw, and Wei Chen · 2019
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Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Driving digital rock towards machine learning: Predicting permeability with gradient boosting and deep neural networks
Oleg Sudakov, Evgeny Burnaev, and Dmitry Koroteev · 2019
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Preferential flow pathways in a deforming granular material: self-organization into functional groups for optimized global transport
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Spatial connectivity of force chains in a simple shear 3d simulation exhibiting shear bands
David M Walker, Antoinette Tordesillas, and Matthew R Kuhn · 2017
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Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, and Alexander Smola · 2017
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Graph reconstruction by discrete morse theory
T. K. Dey, J. Wang, and Y. Wang · 2018
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N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials
Risi Kondor · 2018
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A transfer learning approach for microstructure reconstruction and structure-property predictions
Xiaolin Li, Yichi Zhang, He Zhao, Craig Burkhart, L Catherine Brinson, and Wei Chen · 2018
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Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2018
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Joost H van der Linden, Antoinette Tordesillas, and Guillermo A Narsilio · 2019
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General e (2)-equivariant steerable cnns
Maurice Weiler and Gabriele Cesa · 2019
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Deep scale-spaces: Equivariance over scale
Daniel Worrall and Max Welling · 2019
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Predicting effective diffusivity of porous media from images by deep learning
Haiyi Wu, Wen-Zhen Fang, Qinjun Kang, Wen-Quan Tao, and Rui Qiao · 2019
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Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks
S. Banerjee, L. Magee, D. Wang, X. Li, B. Huo, J. Jayakumar, K. Matho, M. Lin, K. Ram, M. Sivaprakasam, J. Huang, Y. Wang, and P. Mitra · 2020
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A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes
Guang Chen, Zhiqiang Shen, and Ying Li · 2020
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On the universality of rotation equivariant point cloud networks
Nadav Dym and Haggai Maron · 2020
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Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian B Fuchs, Daniel E Worrall, Volker Fischer, and Max Welling · 2020
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github.com/e3nn/e3nn, May 2020
Mario Geiger, Tess Smidt, Benjamin K. Miller, Wouter Boomsma, Kostiantyn Lapchevskyi, Maurice Weiler, Micha? Tyszkiewicz, and Jes Frellsen · 2020
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Inverse-designed spinodoid metamaterials
Siddhant Kumar, Stephanie Tan, Li Zheng, and Dennis M Kochmann · 2020
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A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse pde problems
Xuhui Meng and George Em Karniadakis · 2020
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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Maziar Raissi, Alireza Yazdani, and George Em Karniadakis · 2020
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Poreflow-net: A 3d convolutional neural network to predict fluid flow through porous media
Javier E Santos, Duo Xu, Honggeun Jo, Christopher J Landry, Maša Prodanović, and Michael J Pyrcz · 2020
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Geometric deep learning for computational mechanics part i: Anisotropic hyperelasticity
Nikolaos N Vlassis, Ran Ma, and WaiChing Sun · 2020
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Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Tess E Smidt, Lixin Sun, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, and Boris Kozinsky · 2021
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Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron · 2021
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Dnn2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions
Alexander Fuchs, Yousef Heider, Kun Wang, WaiChing Sun, and Michael Kaliske · 2021
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An offline multi-scale unsaturated poromechanics model enabled by self-designed/self-improved neural networks
Yousef Heider, Hyoung Suk Suh, and WaiChing Sun · 2021
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An immersed phase field fracture model for microporomechanics with darcy–stokes flow
Hyoung Suk Suh and WaiChing Sun · 2021
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Machine learning discovery of high-temperature polymers
Lei Tao, Guang Chen, and Ying Li · 2021
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A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks
Kun Wang, WaiChing Sun, and Qiang Du · 2021
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