80 citations · 271 across the 17 of their papers we have counts for
4 papers · 1 filter
Separable Physics-Informed Neural Networks
Junwoo Cho, Seungtae Nam, Hyunmo Yang +3
Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental lim…
Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
Junwoo Cho, Seungtae Nam, Hyunmo Yang +3
Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forward and inverse problems. While promising, the expensive computational costs to ob…
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…
Meta-Curvature
Eunbyung Park, Junier B. Oliva
We propose meta-curvature (MC), a framework to learn curvature information for better generalization and fast model adaptation. MC expands on the model-agnostic meta-learner (MAML)…