| A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning K Wang, WC Sun Computer Methods in Applied Mechanics and Engineering 334, 337-380, 2018 | 334 | 2018 |
| Meta-modeling game for deriving theory-consistent, microstructure-based traction–separation laws via deep reinforcement learning K Wang, WC Sun Computer Methods in Applied Mechanics and Engineering 346, 216-241, 2019 | 142 | 2019 |
| SO (3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials Y Heider, K Wang, WC Sun Computer Methods in Applied Mechanics and Engineering 363, 112875, 2020 | 125 | 2020 |
| A semi-implicit discrete-continuum coupling method for porous media based on the effective stress principle at finite strain K Wang, WC Sun Computer Methods in Applied Mechanics and Engineering 304, 546-583, 2016 | 89 | 2016 |
| A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation K Wang, WC Sun, Q Du Computational Mechanics 64, 467-499, 2019 | 76 | 2019 |
| An updated Lagrangian LBM–DEM–FEM coupling model for dual-permeability fissured porous media with embedded discontinuities K Wang, WC Sun Computer Methods in Applied Mechanics and Engineering 344, 276-305, 2019 | 63 | 2019 |
| DNN2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions A Fuchs, Y Heider, K Wang, WC Sun, M Kaliske Computers & Structures 249, 106505, 2021 | 62 | 2021 |
| Predicting fault slip via transfer learning K Wang, CW Johnson, KC Bennett, PA Johnson Nature Communications 12 (1), 7319, 2021 | 47 | 2021 |
| Identifying material parameters for a micro-polar plasticity model via X-ray micro-computed tomographic (CT) images: lessons learned from the curve-fitting exercises K Wang, WC Sun, S Salager, SH Na, G Khaddour International Journal for Multiscale Computational Engineering 14 (4), 2016 | 43* | 2016 |
| A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media K Wang, Y Chen, M Mehana, N Lubbers, KC Bennett, Q Kang, ... Journal of Computational Physics 443, 110526, 2021 | 41 | 2021 |
| A unified variational eigen-erosion framework for interacting brittle fractures and compaction bands in fluid-infiltrating porous media K Wang, WC Sun Computer Methods in Applied Mechanics and Engineering 318, 1-32, 2017 | 41 | 2017 |
| Anisotropy of a tensorial Bishop’s coefficient for wetted granular materials K Wang, WC Sun Journal of Engineering Mechanics 143 (3), B4015004, 2017 | 40 | 2017 |
| A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks K Wang, WC Sun, Q Du Computer Methods in Applied Mechanics and Engineering 373, 113514, 2021 | 29 | 2021 |
| Predicting future laboratory fault friction through deep learning transformer models K Wang, CW Johnson, KC Bennett, PA Johnson Geophysical Research Letters, e2022GL098233, 2022 | 28 | 2022 |
| Open-source support toward validating and falsifying discrete mechanics models using synthetic granular materials—Part I: Experimental tests with particles manufactured by a … R Gupta, S Salager, K Wang, WC Sun Acta Geotechnica 14, 923-937, 2019 | 28 | 2019 |
| Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration JE Lee, M Zhu, Z Xi, K Wang, YO Yuan, L Lu Engineering Applications of Computational Fluid Mechanics 18 (1), 2435457, 2024 | 3 | 2024 |
| Automatic speech recognition predicts contemporaneous earthquake fault displacement CW Johnson, K Wang, PA Johnson Nature Communications 16 (1), 1069, 2025 | | 2025 |
| From multiscale modeling to metamodeling of geomechanics problems K Wang Civil Engineering and Engineering Mechanics, Columbia University, 2019 | | 2019 |
