Publications

* denotes equal contribution. Please see the latest update on my Google Scholar page.

Preprints

• 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, O. A. Malik, D. Morozov, O. Azencot, N. B. Erichson, and M. W. Mahoney. (arXiv) [ML + Earthquakes]

• WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning,
  D. Lyu, R. Nakata, P. Ren, M. W. Mahoney, A. Pitarka, N. Nakata, and N. B. Erichson. (arXiv) [ML + Earthquakes]

• Reasoning-Enhanced Object-Centric Learning for Videos,
  J. Li, P. Ren, Y. Liu, and H. Sun. (arXiv) [ML]

• Physics-Informed Machine Learning for Seismic Response Prediction of Nonlinear Steel Moment Resisting Frame Structures,
  R. B. Bond, P. Ren, J. F. Hajjar, and H. Sun. (arXiv) [ML + Earthquakes]

• SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning,
  P. Ren*, N. B. Erichson*, S. Subramanian, O. San, Z. Lukic, and M. W. Mahoney. (arXiv) [ML + PDEs + Climate]

• Physics-informed Neural Network for Seismic Wave Inversion in Layered Semi-infinite Domain,
  P. Ren*, C. Rao*, H. Sun, and Y. Liu. (arXiv) [ML + Earthquakes]

Conference Proceedings

• Model Balancing Helps Low-data Training and Fine-tuning,
  Z. Liu*, Y. Hu*, T. Pang, Y. Zhou, P. Ren, and Y. Yang,
  Empirical Methods in Natural Language Processing (EMNLP), Oral, 2024. (arXiv) [ML + NLP + PDEs]

• Physics-Guided Machine Learning for Structural Metamodeling and Fragility Analysis,
  R. B. Bond, P. Ren, H. Sun, and J. F. Hajjar,
  International Conference on the Behaviour of Steel Structures in Seismic Areas, 2024. (DOI) [ML + Earthquakes]

• Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning,
  W. Chen*, J. Song*, P. Ren, S. Subramanian, D. Morozov, and M. W. Mahoney,
  Neural Information Processing Systems (NeurIPS), 2024,
  ICLR Workshop on AI4Differential Equations In Science, 2024. (arXiv) [ML + PDEs]

• Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs,
  I. Naiman, N. B. Erichson, P. Ren, M. W. Mahoney, and O. Azencot,
  International Conference on Learning Representations (ICLR), 2024. (arXiv) [ML]

• Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning,
  C. Rao*, P. Ren*, Y. Liu, and H. Sun,
  International Conference on Learning Representations (ICLR), 2022. (arXiv) [ML + PDEs]

Journal Publications

• An Unsupervised Machine Learning Approach for Ground Motion Clustering and Selection,
  R. B. Bond, P. Ren, J. F. Hajjar, and H. Sun,
  Earthquake Engineering & Structural Dynamics, 2024. (DOI) (arXiv) [ML + Earthquakes]

• SeismicNet: Physics-informed Neural Networks for Seismic Wave Modeling in Semi-infinite Domain,
  P. Ren*, C. Rao*, H. Sun, and Y. Liu,
  Computer Physics Communications, 2023. (DOI) (arXiv) [ML + Earthquakes]

• PhySR: Physics-informed Deep Super-resolution for Spatiotemporal Data,
  P. Ren, C. Rao, Y. Liu, Z. Ma, Q. Wang, J. X. Wang, and H. Sun,
  Journal of Computational Physics, 2023. (DOI) (arXiv) [ML + PDEs]

• Encoding Physics to Learn Reaction–Diffusion Processes,
  C. Rao*, P. Ren*, Q. Wang, O. Buyukozturk, H. Sun, and Y. Liu,
  Nature Machine Intelligence, 2023. (DOI) (arXiv) [ML + PDEs]

• Clustering and Selection of Hurricane Wind Records Using Autoencoder and k-Means Algorithm,
  X. Du, J. F. Hajjar, R. B. Bond, P. Ren, and H. Sun,
  Journal of Structural Engineering, 2023. (DOI) [ML + Hurricanes]

• Autoregressive Matrix Factorization for Imputation and Forecasting of Spatiotemporal Structural Monitoring Time Series,
  P. Zhang*, P. Ren*, Y. Liu, and H. Sun,
  Mechanical Systems and Signal Processing, 2022. (DOI) [ML + SHM]

• PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs,
  P. Ren, C. Rao, Y. Liu, J. X. Wang, and H. Sun,
  Computer Methods in Applied Mechanics and Engineering, 2022. (DOI) (arXiv) [ML + PDEs]

• Incremental Bayesian Matrix/Tensor Learning for Structural Monitoring Data Imputation and Response Forecasting,
  P. Ren, X. Chen, L. Sun, and H. Sun,
  Mechanical Systems and Signal Processing, 2022. (DOI) [ML + SHM]

• Structural health monitoring of a high-speed railway bridge: five years review and lessons learned,
  Y. Ding, P. Ren, H. Zhao, and C. Miao,
  Smart Structures and Systems, 2018. [ML + SHM]