Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
Spatiotemporal Graph Learning with Direct Volumetric Information Passing and Feature Enhancement
Yuan Mi, Qi Wang, Xueqin Hu +4
Data-driven learning of physical systems has kindled significant attention, where many neural models have been developed. In particular, mesh-based graph neural networks (GNNs) hav…
cs.LG2025
Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flows
Mengtao Yan, Qi Wang, Haining Wang +7
Simulation of fluid flows is crucial for modeling physical phenomena like meteorology, aerodynamics, and biomedicine. Classical numerical solvers often require fine spatiotemporal…
cs.LG2025
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction
Yuan Mi, Pu Ren, Hongteng Xu +6
Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep lear…