4 papers
Achieve What You Imagined: Learning to Align Actions with Visual Plans
Yuheng Qiao, Ziran Wei, Xiaohan Wang +5
World-action models can jointly predict future visual observations and robot actions. However, discrepancies may exist between their visual predictions and the consequences implied…
Transformer refined quantum sampling for strongly correlated electronic structure
Xiongzhi Zeng, Ming Gong, Bowen Kan +18
Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-sca…
Mitigating Gradient Pathology in PINNs through Aligned Constraint
Yichen Luo, Peiyu Zhu, Dongxiao Hu +5
While Physics-Informed Neural Networks (PINNs) are powerful for solving Partial Differential Equations (PDEs), their training is often paralyzed by gradient pathology. The gradient…
MMET: A Multi-Input and Multi-Scale Transformer for Efficient PDEs Solving
Yichen Luo, Jia Wang, Dapeng Lan +2
Partial Differential Equations (PDEs) are fundamental for modeling physical systems, yet solving them in a generic and efficient manner using machine learning-based approaches rema…