3 papers
cs.LG2026
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…
quant-ph2026
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…
cs.LG2025
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…