* and # denote equal contribution and corresponding author, respectively.

Preprints

• Modeling Non-Ergodic Path Effects Using Conditional Generative Model for Fourier Amplitude Spectra,
  M. Lacour, P. Ren, R. Nakata, N. Nakata, and M. Mahoney. [arXiv]

• HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems,
  J. Song, Y. Tang, P. Ren, S. Takayoshi, S. Sawant, Y. Zhu, J. Hu, A. Nonaka, M. W. Mahoney, B. Erichson, and Z. Yao. [arXiv]

• 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]

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

2026

• Spectral Signatures of Large Language Models,
  Z. Zhang*, I. V. Prasad*, Y. Hu, Z. Liu, H. Luo, P. Ren#, and Y. Yang,
  The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), Oral, 2026. [arXiv] [Code]

• Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation,
  X. Liu, S. Shang, X. Wang, P. Ren, and Y. Yang,
  International Conference on Machine Learning (ICML), Spotlight, 2026. [arXiv] [Blog] [Code] [Slides]

• Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization,
  Y. Wang*, Y. Hu*, X. Zhong*, X. Wang*, H. Lu, T. Pang, M. W. Mahoney, Y. Yan, P. Ren#, and Y. Yang#,
  International Conference on Machine Learning (ICML), 2026. [arXiv] [Code]

• Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm,
  S. Wen*, M. Chi*, T. Yu, B. Moseley, M. Y. Michelis, P. Ren, H. Sun, and S. Mishra,
  International Conference on Machine Learning (ICML), 2026. [arXiv] [TensorMesh] [Code]

• RELift: Learned Coarse-to-Fine Propagators for Time-Dependent PDEs with Applications to Electron Dynamics,
  H. Bassi, Y. Zhu, E. Ye, P. Ren, A. Dektor, M. W. Mahoney, H. S. Bhat, and C. Yang,
  APL Computational Physics, 2026. [DOI] [arXiv]

• Learning 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,
  Nature Communications, 2026. [DOI] [arXiv] [Code]

2025

• Rapid wavefield forecasting for earthquake early warning via deep sequence to sequence learning,
  D. Lyu, R. Nakata, P. Ren, M. W. Mahoney, A. Pitarka, N. Nakata, and N. B. Erichson,
  Nature Communications, 2025. [DOI] [arXiv]

• Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction,
  Y. Mi, P. Ren, H. Xu, H. Liu, Z. Wang, Y. Guo, J. Wen, H. Sun, and Y. Liu,
  The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), 2025. [DOI] [arXiv]

• Reasoning-Enhanced Object-Centric Learning for Videos,
  J. Li, P. Ren, Y. Liu, and H. Sun,
  The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), 2025. [DOI] [arXiv]

• Advancing Data-driven Broadband Seismic Wavefield Simulation with Multi-conditional Diffusion Model,
  Z. Bi, N. Nakata, R. Nakata, P. Ren, X. Wu, and M. W. Mahoney,
  IEEE Transactions on Geoscience and Remote Sensing, 2025. [DOI] [arXiv]

• Learning Spatiotemporal Dynamics from Sparse Data via a High-order Physics-encoded Network,
  P. Ren*, J. Song*, C. Rao*, Q. Wang, Y. Guo, H. Sun, and Y. Liu,
  Computer Physics Communications, 2025. [DOI]

• Simulating seismic wavefields using generative artificial intelligence,
  R. Nakata, N. Nakata, P. Ren, Z. Bi, M. Lacour, N. B. Erichson, and M. W. Mahoney,
  The Leading Edge, 2025. [DOI]

• Data-driven modelling of fully nonlinear wave loads on offshore wind-turbine monopiles at prototype scale,
  W. Tan, P. Ren, D. Cao, H. Liang, and H. Chen,
  Marine Structures, 2025. [DOI]

• SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning,
  P. Ren*, N. B. Erichson*, J. Guo, S. Subramanian, O. San, Z. Lukic, and M. W. Mahoney,
  Data-centric Machine Learning Research, 2025. [arXiv]

• FT-SMNet: Fourier transform sparse matrix network for structural health monitoring time series data forecasting,
  W. Wang*, P. Ren*, Y. Liu, L. Meng, H. Liu, H. Liu, and H. Sun,
  Mechanical Systems and Signal Processing, 2025. [DOI]

2024

• 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. [DOI] [arXiv] [Code]

• 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]

• P2C2Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics,
  Q. Wang, P. Ren, H. Zhou, X. Liu, Z. Deng, Y. Zhang, R. Chengze, H. Liu, Z. Wang, J. Wang, H. Sun, and Y. Liu,
  Neural Information Processing Systems (NeurIPS), 2024. [arXiv]

• 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] [Code]

• Forecasting High-dimensional Spatio-Temporal Systems from Sparse Measurements,
  J. Song*, Z. Song*, P. Ren, N. B. Erichson, M. Mahoney, and X. S. Li,
  Machine Learning: Science and Technology, 2024. [DOI]

• 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]

• 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]

2023

• 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]

• 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]

• 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] [Code]

• 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]

2022 and Before

• 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]

• 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]

• 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] [Code]

• 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, 2021. [DOI]

• 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.