11 citations · 19 across the 7 of their papers we have counts for
4 papers · 1 filter
Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of Lithium-Ion Batteries Using Neural Networks
Myeong-Su Lee, Jaemin Oh, Dong-Chan Lee +3
In this work, we address the challenges posed by the high nonlinearity of the Butler-Volmer (BV) equation in forward and inverse simulations of the pseudo-two-dimensional (P2D) mod…
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…
Constant Acceleration Flow
Dogyun Park, Sojin Lee, Sihyeon Kim +3
Rectified flow and reflow procedures have significantly advanced fast generation by progressively straightening ordinary differential equation (ODE) flows. They operate under the a…
Separable Physics-informed Neural Networks for Solving the BGK Model of the Boltzmann Equation
Jaemin Oh, Seung Yeon Cho, Seok-Bae Yun +2
In this study, we introduce a method based on Separable Physics-Informed Neural Networks (SPINNs) for effectively solving the BGK model of the Boltzmann equation. While the mesh-fr…