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math.NA2024★ 9 cited
Deep adaptive sampling for surrogate modeling without labeled data
Xili Wang, Kejun Tang, Jiayu Zhai +2
Surrogate modeling is of great practical significance for parametric differential equation systems. In contrast to classical numerical methods, using physics-informed deep learning…
math.NA2021★ 11 cited
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations
Kejun Tang, Xiaoliang Wan, Chao Yang
In this work we propose a deep adaptive sampling (DAS) method for solving partial differential equations (PDEs), where deep neural networks are utilized to approximate the solution…