533 citations · 623 across the 21 of their papers we have counts for
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cs.LG2026
SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning
Guangyuan Wang, Mads Toftrup, Sebastian Loeschcke +2
Physics-informed neural networks (PINNs) often face ill-conditioned objectives that limit high-accuracy training. Dense quasi-Newton methods improve local conditioning but require…
cs.LG2026
Fourier Feature Pyramids for Physics-Informed Neural Networks
Brandon Zhao, Yixuan Wang, Jonathan T. Barron +3
We present an improved neural field architecture for solving partial differential equations (PDEs). Current physics-informed neural networks (PINNs) provide a flexible framework fo…
math.NA2026
Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
Yixuan Wang
This thesis develops numerical and theoretical approaches for understanding and analyzing singularity formation in Partial Differential Equations (PDEs). The singularity formation…