most citedA unified multimodal understanding and generation model for cross-disciplinary scientific research

1 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

physics.ao-ph2026

Data-driven ensemble prediction of the global ocean

Qiusheng Huang, Xiaohui Zhong, Anboyu Guo +3

Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introdu…

cs.LG2026

AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts

Zijian Zhu, Qiusheng Huang, Anboyu Guo +2

Current AI weather forecasting models predict conventional atmospheric variables but cannot distinguish between cloud microphysical species critical for aviation safety. We introdu…

cs.AI20261 cited

A unified multimodal understanding and generation model for cross-disciplinary scientific research

Xiaomeng Yang, Zhiyu Tan, Xiaohui Zhong +5

Scientific discovery increasingly relies on integrating heterogeneous, high-dimensional data across disciplines nowadays. While AI models have achieved notable success across vario…

physics.ao-ph2025

A data-driven global ocean forecasting model with sub-daily and eddy-resolving resolution

Yuan Niu, Qiusheng Huang, Xiaohui Zhong +7

High-fidelity ocean forecasting at high spatial and temporal resolution is essential for capturing fine-scale dynamical features, with profound implications for hazard prediction,…

cs.LG2025

FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution

Qiusheng Huang, Yuan Niu, Xiaohui Zhong +5

Accurate, high-resolution ocean forecasting is crucial for maritime operations and environmental monitoring. While traditional numerical models are capable of producing sub-daily,…

physics.ao-ph20251 cited

FuXi-RTM: A Physics-Guided Prediction Framework with Radiative Transfer Modeling

Qiusheng Huang, Xiaohui Zhong, Xu Fan +2

Similar to conventional video generation, current deep learning-based weather prediction frameworks often lack explicit physical constraints, leading to unphysical outputs that lim…