Spatio-temporal fluid dynamics modeling via physical-awareness and parameter diffusion guidance
H Wu, F Xu, Y Duan, Z Niu, W Wang, G Lu… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …
Get Rid of Task Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
Spatiotemporal learning has become a pivotal technique to enable urban intelligence.
Traditional spatiotemporal models mostly focus on a specific task by assuming a same …
Traditional spatiotemporal models mostly focus on a specific task by assuming a same …
Enhancing Traffic Flow Forecasting With Delay Propagation: Adaptive Graph Convolution Networks for Spatio-Temporal Data
Z Yingran, L Chao, S Rui - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
Traffic flow Forecasting is essential in intelligent transportation systems. Although graph
neural networks perform well with non-Euclidean traffic data, they exhibit limits in accurately …
neural networks perform well with non-Euclidean traffic data, they exhibit limits in accurately …
Pattern-oriented Attention Mechanism for Multivariate Time Series Forecasting
Multivariate time series forecasting is applied in many domains, such as finance,
transportation and industry. The main challenge of precise forecasting lies in accurately …
transportation and industry. The main challenge of precise forecasting lies in accurately …
Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
Spatiotemporal learning has become a pivotal technique to enable urban intelligence.
Traditional spatiotemporal models mostly focus on a specific task by assuming a same …
Traditional spatiotemporal models mostly focus on a specific task by assuming a same …
Improving Generalization of Dynamic Graph Learning via Environment Prompt
Out-of-distribution (OOD) generalization issue is a well-known challenge within deep
learning tasks. In dynamic graphs, the change of temporal environments is regarded as the …
learning tasks. In dynamic graphs, the change of temporal environments is regarded as the …
P-Align: Self-Alignment in Physical Dynamical System Modeling
Z Xu, F Xu, H Wang, X ZHOU, L Peng, Q Wen, K Wang… - openreview.net
Deep learning has emerged as the new paradigm in modeling complex physical dynamical
systems. Nevertheless, data-driven methods learn patterns by optimizing statistical metrics …
systems. Nevertheless, data-driven methods learn patterns by optimizing statistical metrics …