43 citations · 44 across the 3 of their papers we have counts for
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cs.LG2026
From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
Jianan Yang, Yiran Wang, Shuai Li +3
Physics-informed neural networks (PINNs) offer a mesh-free framework for solving partial differential equations (PDEs), yet training often suffers from gradient pathologies, spectr…
cs.LG2021★ 43 cited
A critical look at the current train/test split in machine learning
Jimin Tan, Jianan Yang, Sai Wu +2
The randomized or cross-validated split of training and testing sets has been adopted as the gold standard of machine learning for decades. The establishment of these split protoco…