Search Results for author: Hanqi Zhuang

Found 4 papers, 0 papers with code

ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting

no code implementations12 Aug 2021 Yu Huang, James Li, Min Shi, Hanqi Zhuang, Xingquan Zhu, Laurent Chérubin, James VanZwieten, Yufei Tang

A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system.

Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems

no code implementations11 Aug 2021 Yu Huang, Yufei Tang, Xingquan Zhu, Min Shi, Ali Muhamed Ali, Hanqi Zhuang, Laurent Cherubin

To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics.

Active Learning Spatio-Temporal Forecasting

Physics-informed Tensor-train ConvLSTM for Volumetric Velocity Forecasting of Loop Current

no code implementations4 Aug 2020 Yu Huang, Yufei Tang, Hanqi Zhuang, James VanZwieten, Laurent Cherubin

According to the National Academies, a weekly forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies is critical for understanding the oceanography and ecosystem, and for mitigating outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM).

Temporal Sequences Video Prediction

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