Home
Publications
Activities

Pu Ren

alt text 

I am a Postdoc Fellow in Machine Learning and Analytics Group at Lawrence Berkeley National Lab, where I am working with Prof. Michael Mahoney. I received my Ph.D. at Northeastern University in July 2022, where I worked with Prof. Hao Sun and Prof. Ryan Wang. I was also fortunate to collaborate with Prof. Jerome F. Hajjar, Prof. Yang Liu, and Prof. Jian-Xun Wang.

I am broadly interested in AI methods for scientific problems. I am working on both the methodology and application sides of scientific machine learning. Specifically, I work on soft and hard-constrained machine learning, super-resolution, and generative modeling. I apply these methods to many scientific domains, e.g., fluid dynamics, climate science, geophysics, and earthquake engineering.

Email: pren AT lbl DOT gov

Recent News

  • [07/2024] We are organizing a workshop at NeurIPS 2024 on Foundation Models for Science. We welcome submissions on scientific foundation models, AI for Science, and Scientific Machine Learning (SciML). See more details using this Link.

  • [07/2024] Our work on conditional generative modeling for earthquakes is online now. (arXiv)

  • [06/2024] WaveCastNet is online now. We proposed an AI-enabled wavefield forecasting framework for earthquake early warning. (arXiv)

  • [05/2024] I presented our recent work on conditional VAE models for ground motion modeling at Seismological Society of America 2024.

  • [05/2024] Our work on data-efficient pretraining and in-context learning for SciML has been accepted by ICLR 2024 Workshop on AI4Differential Equations In Science. (arXiv)

  • [04/2024] Bailey has put our PhD work online. We investigated the physics-informed ML for seismic response modeling. (arXiv)

  • [01/2024] Our work on generative models for time series data has been accepted by ICLR 2024. (arXiv)

  • [12/2023] I presented our recent work on Deep Generative Models for Earthquake Ground Motion Simulation at AGU 2023.

  • [12/2023] Our work on ground‐motion spectra clustering and selection has been published on Earthquake Engineering & Structural Dynamics. (DOI) (arXiv)

  • [11/2023] Our work on physics-informed deep learning for seismic wave propagation has been published in the Computer Physics Communications. (DOI) (arXiv)

  • [08/2023] Our work on physics-informed super-resolution for spatiotemporal data has been published in the Journal of Computational Physics. (DOI) (arXiv)

  • [06/2023] Our work SuperBench is online now. We build a super-resolution benchmark for scientific machine learning! (arXiv)

  • [06/2023] Our work on encoding physics to learn reaction-diffusion processes has been accepted by Nature Machine Intelligence. (DOI) (arXiv)

  • [04/2023] Our work on seismic inversion using PINNs is online now. (arXiv)

Selected Awards

  • [05/2021] Second Place of 2021 EMI Dynamics Committee Student Paper Competition (Link)

  • [05/2020] Third Place of 2020 EMI SHM&C Committee Student Paper Competition (Link)

  • [09/2019] Vilas Mujumdar Fellowship

  • [10/2018] China National Scholarship

  • [04/2015] Finalist of Mathematical Contest in Modeling (top 0.5%)