Publications

• Deep Generative Data Assimilation in Multimodal Setting
  Yongquan Qu*, Juan Nathaniel*, Shuolin Li, Pierre Gentine, (2024),
  CVPR 2024 Workshop EarthVision 2024 *Equal Contribution ArXiv 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 ArXiv

• 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),
  arXiv preprint arXiv:2402.00712 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. Link Code

Conference Presentations

• Qu, Y and X. Shi, 2022: Model-consistent Parameterisation With Deep Learning and Differentiable Physics. Asia Oceania Geoscience Society 19th Annual Meeting (AOGS’22), AS14-A006
• Qu, Y and X. Shi, 2020: Data-Driven Turbulence Modelling for Two-Dimensional Barotropic Flow Using Neural Networks. American Geophysical Union Fall Meeting (AGU’20), A059-0009