2 papers
cs.LG2024
PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations
Namgyu Kang, Jaemin Oh, Youngjoon Hong +1
The numerical approximation of partial differential equations (PDEs) using neural networks has seen significant advancements through Physics-Informed Neural Networks (PINNs). Despi…
cs.LG2022
PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers
Namgyu Kang, Byeonghyeon Lee, Youngjoon Hong +2
With the increases in computational power and advances in machine learning, data-driven learning-based methods have gained significant attention in solving PDEs. Physics-informed n…