1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Architecture--Optimization Co-Design for Physics-Informed Neural Networks via Layer-wise Coordinate Adaptation and Gradient Conflict Resolution
Pancheng Niu, Jun Guo, Qiaolin He +2
Physics-informed neural networks (PINNs) can be limited by coordinate representations and conflicting gradients from heterogeneous physical constraints. We propose Architecture--Co…
cs.LG2024
Improved physics-informed neural network in mitigating gradient related failures
Pancheng Niu, Yongming Chen, Jun Guo +3
Physics-informed neural networks (PINNs) integrate fundamental physical principles with advanced data-driven techniques, driving significant advancements in scientific computing. H…
cs.LG2022★ 1 cited
A Study on Transformer Configuration and Training Objective
Fuzhao Xue, Jianghai Chen, Aixin Sun +6
Transformer-based models have delivered impressive results on many tasks, particularly vision and language tasks. In many model training situations, conventional configurations are…