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cs.AI2025
Lang-PINN: From Language to Physics-Informed Neural Networks via a Multi-Agent Framework
Xin He, Liangliang You, Hongduan Tian +3
Physics-informed neural networks (PINNs) provide a powerful approach for solving partial differential equations (PDEs), but constructing a usable PINN remains labor-intensive and e…
cs.AI2025
Active Sampling for Node Attribute Completion on Graphs
Benyuan Liu, Xu Chen, Yanfeng Wang +3
Node attribute, a type of crucial information for graph analysis, may be partially or completely missing for certain nodes in real world applications. Restoring the missing attribu…