1 citations · 1 across the 4 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Learning efficient representations of complex constraints for scalable optimization
Yilin Wen, Yi Guo, Bo Zhao +4
Complex constraints often make real-world optimization computationally prohibitive at the scale and speed required for operational decision-making. Here we introduce PolyFormer, a…
cs.LG2025★ 1 cited
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…
cs.LG2024
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…