5 papers
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…
False Data-Injection Attack Detection in Cyber-Physical Systems: A Wasserstein Distributionally Robust Reachability Optimization Approach
Yulin Feng, Dapeng Lan, Chao Shang
Cyber-physical system (CPS) is the foundational backbone of modern critical infrastructures, so ensuring its security and resilience against cyber-attacks is of pivotal importance.…
Transferring Vision-Language-Action Models to Industry Applications: Architectures, Performance, and Challenges
Shuai Li, Chen Yizhe, Li Dong +4
The application of artificial intelligence (AI) in industry is accelerating the shift from traditional automation to intelligent systems with perception and cognition. Vision langu…
Liaohe-CobotMagic-PnP: an Imitation Learning Dataset of Intelligent Robot for Industrial Applications
Chen Yizhe, Wang Qi, Hu Dongxiao +10
In Industry 4.0 applications, dynamic environmental interference induces highly nonlinear and strongly coupled interactions between the environmental state and robotic behavior. Ef…
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…