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cs.LG2025
Efficient Global-Local Fusion Sampling for Physics-Informed Neural Networks
Jiaqi Luo, Shixin Xu, Zhouwang Yang
The accuracy of Physics-Informed Neural Networks (PINNs) critically depends on the placement of collocation points, as the PDE loss is approximated through sampling over the soluti…
cs.CV2025★ 1 cited
TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning
Jiaqi Luo, Yuan Yuan, Shixin Xu
Tabular-image multimodal learning, which integrates structured tabular data with imaging data, holds great promise for a variety of tasks, especially in medical applications. Yet,…
cs.LG2025
An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks
Jiaqi Luo, Yahong Yang, Yuan Yuan +2
This paper introduces Residual-based Smote (RSmote), an innovative local adaptive sampling technique tailored to improve the performance of Physics-Informed Neural Networks (PINNs)…