40 citations · 209 across the 49 of their papers we have counts for
5 papers · 1 filter
Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs
Seungchan Ko, Seok-Bae Yun, Youngjoon Hong
In this paper, we perform the convergence analysis of unsupervised Legendre--Galerkin neural networks (ULGNet), a deep-learning-based numerical method for solving partial different…
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…
Physics-Informed Convolutional Transformer for Predicting Volatility Surface
Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae +2
Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The Black-Scholes opt…
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…
BGK model for multi-component gases near a global Maxwellian
Gi-Chan Bae, Christian Klingenberg, Marlies Pirner +1
In this paper, we establish the existence of the unique global-in-time classical solutions to the multi-component BGK model suggested in \cite{mixmodel} when the initial data is a…