Výsledky vyhledávání - "Qu, Yongquan"

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Deep Generative Data Assimilation in Multimodal Setting

Autor: Qu, Yongquan, Nathaniel, Juan, Li, Shuolin, Gentine, Pierre

Robust integration of physical knowledge and data is key to improve computational simulations, such as Earth system models. Data assimilation is crucial for achieving this goal because it provides a systematic framework to calibrate model outputs wit

Externí odkaz: http://arxiv.org/abs/2404.06665

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Joint Parameter and Parameterization Inference with Uncertainty Quantification through Differentiable Programming

Autor: Qu, Yongquan, Bhouri, Mohamed Aziz, Gentine, Pierre

Accurate representations of unknown and sub-grid physical processes through parameterizations (or closure) in numerical simulations with quantified uncertainty are critical for resolving the coarse-grained partial differential equations that govern m

Externí odkaz: http://arxiv.org/abs/2403.02215

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ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction

Autor: Nathaniel, Juan, Qu, Yongquan, Nguyen, Tung, Yu, Sungduk, Busecke, Julius, Grover, Aditya, Gentine, Pierre

Accurate prediction of climate in the subseasonal-to-seasonal scale is crucial for disaster preparedness and robust decision making amidst climate change. Yet, forecasting beyond the weather timescale is challenging because it deals with problems oth

Externí odkaz: http://arxiv.org/abs/2402.00712

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