13 papers
On a derived equivalence classification of weakly symmetric algebras of Euclidean type
Hideto Asashiba, Ayako Itaba, Manuel Saorin
We consider a family of weakly symmetric algebras of Euclidean type over an algebraically closed field , where $\Bbbk^\times:= \Bbbk\setminus \{0…
VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecasting
Zhisheng Chen, Jinhan Li, Yuxuan Li +6
Global medium-range weather forecasting requires modeling structured yet state-dependent interactions among heterogeneous atmospheric fields. Existing data-driven models largely le…
Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting
Fan Xu, Yuan Gao, Kun Wang +4
Probabilistic weather forecasting requires not only accurate trajectories, but calibrated distributions over plausible atmospheric futures. Recent data-driven systems have achieved…
DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting
Huanshuo Dong, Hao Wu, Hong Wang +2
Long-term fluid dynamics forecasting is a critically important problem in science and engineering. While neural operators have emerged as a promising paradigm for modeling systems…
NeuralOGCM: Differentiable Ocean Modeling with Learnable Physics
Hao Wu, Yuan Gao, Fan Xu +4
High-precision scientific simulation faces a long-standing trade-off between computational efficiency and physical fidelity. To address this challenge, we propose NeuralOGCM, an oc…
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…
VISION: Prompting Ocean Vertical Velocity Reconstruction from Incomplete Observations
Yuan Gao, Hao Wu, Qingsong Wen +3
Reconstructing subsurface ocean dynamics, such as vertical velocity fields, from incomplete surface observations poses a critical challenge in Earth science, a field long hampered…
Differential-Integral Neural Operator for Long-Term Turbulence Forecasting
Hao Wu, Yuan Gao, Fan Xu +5
Accurately forecasting the long-term evolution of turbulence represents a grand challenge in scientific computing and is crucial for applications ranging from climate modeling to a…
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…
Advanced Long-term Earth System Forecasting
Hao Wu, Yuan Gao, Ruijian Gou +30
Reliable long-term forecasting of Earth system dynamics is fundamentally limited by instabilities in current artificial intelligence (AI) models during extended autoregressive simu…
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…
Improved Forecasts of Global Extreme Marine Heatwaves Through a Physics-guided Data-driven Approach
Ruiqi Shu, Hao Wu, Yuan Gao +3
The unusually warm sea surface temperature events known as marine heatwaves (MHWs) have a profound impact on marine ecosystems. Accurate prediction of extreme MHWs has significant…