From the 1 of 10 linked papers with an AI index.
10 papers
Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation
Ruiqi Shu, Ruijian Gou, Yanfei Xiang +1
The paper introduces Ocean-E2E, a hybrid physics‑based and data‑driven framework that uses end‑to‑end neural data assimilation to forecast global extreme marine heatwaves up to 40…
HybridOM: Hybrid Physics-Based and Data-Driven Global Ocean Modeling with Efficient Spatial Downscaling
Ruiqi Shu, Xiaohui Zhong, Qiusheng Huang +4
Global ocean modeling is vital for climate science but struggles to balance computational efficiency with accuracy. Traditional numerical solvers are accurate but computationally e…
NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation
Yuan Gao, Hao Wu, Fan Xu +7
Long-term, high-fidelity simulation of slow-changing physical systems, such as the ocean and climate, presents a fundamental challenge in scientific computing. Traditional autoregr…
An Exterior-Embedding Neural Operator Framework for Preserving Conservation Laws
Huanshuo Dong, Hong Wang, Hao Wu +5
Neural operators have demonstrated considerable effectiveness in accelerating the solution of time-dependent partial differential equations (PDEs) by directly learning governing ph…
Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias
Hao Wu, Yuan Gao, Chang Liu +11
Accurately predicting the long-term evolution of turbulence is crucial for advancing scientific understanding and optimizing engineering applications. However, existing deep learni…
Advancing Ocean State Estimation with efficient and scalable AI
Yanfei Xiang, Yuan Gao, Hao Wu +5
Accurate and efficient global ocean state estimation remains a grand challenge for Earth system science, hindered by the dual bottlenecks of computational scalability and degraded…