no code implementations • 10 Mar 2024 • Jaemin Oh, Seung Yeon Cho, Seok-Bae Yun, Eunbyung Park, Youngjoon Hong
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.
no code implementations • 16 Nov 2022 • 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 differential equations (PDEs).
no code implementations • 16 Nov 2022 • Junwoo Cho, Seungtae Nam, Hyunmo Yang, Seok-Bae Yun, Youngjoon Hong, Eunbyung Park
SPINN operates on a per-axis basis instead of point-wise processing in conventional PINNs, decreasing the number of network forward passes.
1 code implementation • 22 Sep 2022 • Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae, Muhyun Lee, Youngjoon Hong
The Black-Scholes option pricing model is one of the most widely used models by market participants.
2 code implementations • 26 Jul 2022 • Namgyu Kang, Byeonghyeon Lee, Youngjoon Hong, Seok-Bae Yun, Eunbyung Park
With the increases in computational power and advances in machine learning, data-driven learning-based methods have gained significant attention in solving PDEs.
no code implementations • 15 Dec 2020 • Stephane Brull, Seok-Bae Yun
In the non-critical case $(-1/2<\nu<1)$, we utilize the property that the temperature tensor is equivalent to the temperature in this range.
Analysis of PDEs
