2 papers
cs.LG2026
Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation
Xujia Chen, Xinyue Hu, Letian Chen +2
Physics-Informed Neural Networks (PINNs) combine deep learning with physical constraints for solving partial differential equations (PDEs), and are widely applied in fluid mechanic…
cs.LG2024
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
Xudong Guo, Daming Shi, Junjie Yu +1
The emergence of multi-agent reinforcement learning (MARL) is significantly transforming various fields like autonomous vehicle networks. However, real-world multi-agent systems ty…