Publications

• Physically Consistent Global Atmospheric Data Assimilation with Machine Learning in a Latent Space
  Hang Fan,Ben Fei, Pierre Gentine, Yi Xiao, Kun Chen, Yubao Liu, Yongquan Qu, Fenghua Ling, Lei Bai (2025)
  Pre-print arxiv link

• Machine-Assisted Physical Closure for Coarse Suspended Sediments in Vegetated Turbulent Channel Flows
  Shuolin Li, Yongquan Qu, Tian Zheng, Pierre Gentine, (2024)
  Geophysical Research Letters 51 (20), e2024GL110475, paper

• Deep Generative Data Assimilation in Multimodal Setting
  Yongquan Qu*, Juan Nathaniel*, Shuolin Li, Pierre Gentine, (2024),
  Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 449-459 (🎉CVPR EarthVision 2024 Workshop Best Student Paper Award 🎉) *Equal Contribution paper Code

• Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming
  Yongquan Qu, Mohamed Aziz Bhouri, Pierre Gentine, (2024),
  ICLR 2024 Workshop on AI4DifferentialEquations In Science paper

• ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
  Juan Nathaniel, Yongquan Qu, Tung Nguyen, Sungduk Yu, Julius Busecke, Aditya Grover, Pierre Gentine, (2024),
  Advances in Neural Information Processing Systems 37, 43715–43729 Homepage

• Can a Machine-Learning-Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?
  Yongquan Qu, Xiaoming Shi, (2023),
  Artificial Intelligence for the Earth Systems, 2 (1), e220050. paper Code