8 papers
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…
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…
Cracking the Code of Arctic Sea Ice: Why Models Fail to Predict Its Retreat?
Ruijian Gou, Gerrit Lohmann, Deliang Chen +4
Arctic sea ice is rapidly retreating due to global warming, and emerging evidence suggests that the rate of decline may have been underestimated. A key factor contributing to this…
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…
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…
BeamVQ: Beam Search with Vector Quantization to Mitigate Data Scarcity in Physical Spatiotemporal Forecasting
Weiyan Wang, Xingjian Shi, Ruiqi Shu +10
In practice, physical spatiotemporal forecasting can suffer from data scarcity, because collecting large-scale data is non-trivial, especially for extreme events. Hence, we propose…