5 papers
PolyFormer: learning efficient reformulations for scalable optimization under complex physical constraints
Yilin Wen, Yi Guo, Bo Zhao +4
Real-world optimization problems are often constrained by complex physical laws that limit computational scalability. These constraints are inherently tied to complex regions, and…
DC-W2S: Dual-Consensus Weak-to-Strong Training for Reliable Process Reward Modeling in Biological Reasoning
Chi-Min Chan, Ehsan Hajiramezanali, Xiner Li +6
In scientific reasoning tasks, the veracity of the reasoning process is as critical as the final outcome. While Process Reward Models (PRMs) offer a solution to the coarse-grained…
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…
Discovering physical laws with parallel symbolic enumeration
Kai Ruan, Yilong Xu, Ze-Feng Gao +4
Symbolic regression plays a crucial role in modern scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data. A key c…
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…