| PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs P Ren, C Rao, Y Liu, JX Wang, H Sun Computer Methods in Applied Mechanics and Engineering 389, 114399, 2022 | 143 | 2022 |
| Encoding physics to learn reaction–diffusion processes C Rao*, P Ren*, Q Wang, O Buyukozturk, H Sun, Y Liu Nature Machine Intelligence 5, 765–779, 2023 | 52* | 2023 |
| Incremental Bayesian matrix/tensor learning for structural monitoring data imputation and response forecasting P Ren, X Chen, L Sun, H Sun Mechanical Systems and Signal Processing 158, 107734, 2021 | 45 | 2021 |
| Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning C Rao*, P Ren*, Y Liu, H Sun The Tenth International Conference on Learning Representations (ICLR 2022), 2022 | 26 | 2022 |
| Structural health monitoring of a high-speed railway bridge: five years review and lessons learned Y Ding, P Ren, H Zhao, CQ Miao Smart Struct. Syst 21 (5), 695-703, 2018 | 23 | 2018 |
| SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain P Ren, C Rao, S Chen, JX Wang, H Sun, Y Liu Computer Physics Communications 295, 109010, 2024 | 22 | 2024 |
| PhySR: Physics-informed deep super-resolution for spatiotemporal data P Ren, C Rao, Y Liu, Z Ma, Q Wang, JX Wang, H Sun Journal of Computational Physics 492, 112438, 2023 | 21 | 2023 |
| Autoregressive matrix factorization for imputation and forecasting of spatiotemporal structural monitoring time series P Zhang*, P Ren*, Y Liu, H Sun Mechanical Systems and Signal Processing 169, 108718, 2022 | 16 | 2022 |
| Superbench: A super-resolution benchmark dataset for scientific machine learning P Ren, NB Erichson, S Subramanian, O San, Z Lukic, MW Mahoney arXiv preprint arXiv:2306.14070, 2023 | 6 | 2023 |
| An unsupervised machine learning approach for ground‐motion spectra clustering and selection RB Bond, P Ren, JF Hajjar, H Sun Earthquake Engineering & Structural Dynamics 53 (3), 1107-1124, 2024 | 5* | 2024 |
| Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs I Naiman, NB Erichson, P Ren, MW Mahoney, O Azencot The Twelfth International Conference on Learning Representations (ICLR 2024), 2023 | 5 | 2023 |
| Physics-informed neural network for seismic wave inversion in layered semi-infinite domain P Ren, C Rao, H Sun, Y Liu arXiv preprint arXiv:2305.05150, 2023 | 4 | 2023 |
| Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning W Chen, J Song, P Ren, S Subramanian, D Morozov, MW Mahoney arXiv preprint arXiv:2402.15734, 2024 | 3 | 2024 |
| Reasoning-Enhanced Object-Centric Learning for Videos J Li, P Ren, Y Liu, H Sun arXiv preprint arXiv:2403.15245, 2024 | 1 | 2024 |
| Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures RB Bond, P Ren, JF Hajjar, H Sun arXiv preprint arXiv:2402.17992, 2024 | 1 | 2024 |
| Clustering and Selection of Hurricane Wind Records Using Autoencoder and -Means Algorithm X Du, JF Hajjar, RB Bond, P Ren, H Sun Journal of Structural Engineering 149 (8), 04023096, 2023 | 1 | 2023 |
| Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling P Ren, R Nakata, M Lacour, I Naiman, N Nakata, J Song, Z Bi, OA Malik, ... arXiv preprint arXiv:2407.15089, 2024 | | 2024 |
| Physics-Guided Machine Learning for Structural Metamodeling and Fragility Analysis RB Bond, P Ren, H Sun, JF Hajjar International Conference on the Behaviour of Steel Structures in Seismic …, 2024 | | 2024 |
| WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning D Lyu, R Nakata, P Ren, MW Mahoney, A Pitarka, N Nakata, NB Erichson arXiv preprint arXiv:2405.20516, 2024 | | 2024 |
| Deep Generative Models for Earthquake Ground Motion Simulation P Ren, M Lacour, MCA White, R Nakata, N Nakata, OA Malik, D Morozov, ... AGU Fall Meeting Abstracts 2023, S41B-03, 2023 | | 2023 |
