Automated modeling of random inclusion composites. - Mahesh Bailakanavar; Yang Liu 130; Jacob Fish; Yuan Zheng (Eng. Comput., 2014)

Statistical Regularization for Identification of Structural Parameters and External Loadings Using State Space Models. - Hao Sun 002; Dongming Feng; Yang Liu 130; Maria Q. Feng (Comput. Aided Civ. Infrastructure Eng., 2015)

Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling. - Ruiyang Zhang; Yang Liu 130; Hao Sun 002 (CoRR, 2019)

Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization. - Chengping Rao; Yang Liu 130 (CoRR, 2020)

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures. - Ruiyang Zhang; Yang Liu 130; Hao Sun 002 (CoRR, 2020)

Physics-informed deep learning for incompressible laminar flows. - Chengping Rao; Hao Sun 002; Yang Liu 130 (CoRR, 2020)

Deep learning of physical laws from scarce data. - Zhao Chen; Yang Liu 130; Hao Sun 002 (CoRR, 2020)

Physics informed deep learning for computational elastodynamics without labeled data. - Chengping Rao; Hao Sun 002; Yang Liu 130 (CoRR, 2020)

Physics-informed Spline Learning for Nonlinear Dynamics Discovery. - Fangzheng Sun; Yang Liu 130; Hao Sun 002 (IJCAI, 2021)

Hard Encoding of Physics for Learning Spatiotemporal Dynamics. - Chengping Rao; Hao Sun 002; Yang Liu 130 (CoRR, 2021)

Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos. - Lele Luan; Yang Liu 130; Hao Sun 002 (CoRR, 2021)

Embedding Physics to Learn Spatiotemporal Dynamics from Sparse Data. - Chengping Rao; Hao Sun 002; Yang Liu 130 (CoRR, 2021)

PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs. - Pu Ren; Chengping Rao; Yang Liu 130; Jian-Xun Wang 001; Hao Sun 002 (CoRR, 2021)

Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning. - Chengping Rao; Pu Ren; Yang Liu 130; Hao Sun 002 (ICLR, 2022)

Distilling Governing Laws and Source Input for Dynamical Systems from Videos. - Lele Luan; Yang Liu 130; Hao Sun 002 (IJCAI, 2022)

Physics-informed Deep Super-resolution for Spatiotemporal Data. - Pu Ren; Chengping Rao; Yang Liu 130; Zihan Ma 002; Qi Wang; Jian-Xun Wang 001; Hao Sun 002 (CoRR, 2022)

SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain. - Pu Ren; Chengping Rao; Su Chen; Jian-sun Wang; Hao Sun 002; Yang Liu 130 (CoRR, 2022)

Forecasting of nonlinear dynamics based on symbolic invariance. - Zhao Chen; Yang Liu 130; Hao Sun 002 (Comput. Phys. Commun., 2022)

Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search. - Fangzheng Sun; Yang Liu 130; Jian-Xun Wang 001; Hao Sun 002 (ICLR, 2023)

Encoding physics to learn reaction-diffusion processes. - Chengping Rao; Pu Ren; Qi Wang; Oral Buyukozturk; Hao Sun 002; Yang Liu 130 (Nat. Mac. Intell., 2023)

PhySR: Physics-informed deep super-resolution for spatiotemporal data. - Pu Ren; Chengping Rao; Yang Liu 130; Zihan Ma 002; Qi Wang; Jian-Xun Wang 001; Hao Sun 002 (J. Comput. Phys., 2023)

P2C2Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics. - Qi Wang; Pu Ren; Hao Zhou; Xin-Yang Liu; Zhiwen Deng; Yi Zhang; Zeruizhi Cheng; Hongsheng Liu 002; Zidong Wang 010; Jian-Xun Wang 001; Ji-Rong Wen; Hao Sun 002; et al. (NeurIPS, 2024)

P2C2Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics. - Qi Wang; Pu Ren; Hao Zhou; Xin-Yang Liu; Zhiwen Deng; Yi Zhang; Ruizhi Chengze; Hongsheng Liu 002; Zidong Wang 010; Jian-Xun Wang 001; Ji-Rong Wen; Hao Sun 002; et al. (CoRR, 2024)

SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain. - Pu Ren; Chengping Rao; Su Chen; Jian-Xun Wang 001; Hao Sun 002; Yang Liu 130 (Comput. Phys. Commun., 2024)

Reasoning-Enhanced Object-Centric Learning for Videos. - Jian Li; Pu Ren; Yang Liu 130; Hao Sun 002 (KDD (1), 2025)