5 papers
Structured Neural Modeling of Daily Arctic Sea-Ice Concentration Evolution: Physical-Trajectory-Driven Learning and Forecast-Domain Adaptation
Maqun Zhang, Feng Gao, Wankun Chen +3
Accurate modeling of the daily evolution of sea ice concentration (SIC) is central to improving the credibility and operational forecasting capability of deep learning-based sea ic…
One-Step Evolution for Long-Time Extrapolation: An Error-Bound-Informed and Prior-Guided Neural Residual Framework for Autonomous PDEs
Maqun Zhang, Feng Gao, Wankun Chen +3
Accurate simulation of the long-time evolution of systems governed by partial differential equations (PDEs) is central to scientific computing. Among existing deep learning?based a…
Physics-Knowledge-Guided Hybrid Neural Learning for Arctic Sea Ice Concentration Evolution and Short-Range Prediction
Maqun Zhang, Feng Gao, Wankun Chen +3
Accurate modeling of sea ice concentration (SIC) evolution is essential for polar climate assessment and short?range sea ice prediction. Numerical and data-driven approaches consti…
Incomplete Observations Boost Evolutionary Performance in Ocean Modeling
Yangyang Kong, Yutong Jiang, Yanhai Gan +3
Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing computational constraints and limiting model p…
Explore the Ideology of Deep Learning in ENSO Forecasts
Yanhai Gan, Yipeng Chen, Ning Li +3
The El Ni{~n}o-Southern Oscillation (ENSO) exerts profound influence on global climate variability, yet its prediction remains a grand challenge. Recent advances in deep learning h…