Namgyu Kang — accepted AI-conference papers

2025

PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations

Namgyu Kang, Jaemin Oh, Youngjoon Hong, Eunbyung Park

ICLR 2025poster

The numerical approximation of partial differential equations (PDEs) using neural networks has seen significant advancements through Physics-Informed Neural Networks (PINNs). Despite their straightforward optimization framework and flexibility in implementing various PDEs, PINNs often suffer from li…

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2023

PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers

Namgyu Kang, Byeonghyeon Lee, Youngjoon Hong, Seok-Bae Yun, Eunbyung Park

AAAI 2023technical

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 neural networks (PINNs) have recently emerged and succeeded in various forward and inverse PDE problems thanks to their e…

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