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 • 3 Oct 2023 • JunHo Choi, Taehyun Yun, Namjung Kim, Youngjoon Hong
In this paper, we introduce the Spectral Coefficient Learning via Operator Network (SCLON), a novel operator learning-based approach for solving parametric partial differential equations (PDEs) without the need for data harnessing.
1 code implementation • 13 Sep 2023 • Sanghyeon Kim, Hyunmo Yang, Younghyun Kim, Youngjoon Hong, Eunbyung Park
The recent surge in large-scale foundation models has spurred the development of efficient methods for adapting these models to various downstream tasks.
no code implementations • 9 Aug 2023 • Jae Yong Lee, Seungchan Ko, Youngjoon Hong
Partial differential equations (PDEs) underlie our understanding and prediction of natural phenomena across numerous fields, including physics, engineering, and finance.
no code implementations • 20 Jul 2023 • Soohan Kim, Jimyeong Kim, Hong Kee Sul, Youngjoon Hong
The purpose of this research is to devise a tactic that can closely track the daily cumulative volume-weighted average price (VWAP) using reinforcement learning.
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 • 15 Oct 2022 • Byeongkeun Ahn, Chiyoon Kim, Youngjoon Hong, Hyunwoo J. Kim
Normalizing flows model probability distributions by learning invertible transformations that transfer a simple distribution into complex distributions.
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.
no code implementations • 19 Aug 2022 • Gung-Min Gie, Youngjoon Hong, Chang-Yeol Jung
We propose a new semi-analytic physics informed neural network (PINN) to solve singularly perturbed boundary value problems.
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 • 21 Jul 2022 • JunHo Choi, Namjung Kim, Youngjoon Hong
Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems.
no code implementations • 24 Oct 2020 • Bryce Chudomelka, Youngjoon Hong, Hyunwoo Kim, Jinyoung Park
Nonlinear differential equations are challenging to solve numerically and are important to understanding the dynamics of many physical systems.
1 code implementation • ECCV 2020 • Byungjoo Kim, Bryce Chudomelka, Jinyoung Park, Jaewoo Kang, Youngjoon Hong, Hyunwoo J. Kim
Motivated by the SSP property and a generalized Runge-Kutta method, we propose Strong Stability Preserving networks (SSP networks) which improve robustness against adversarial attacks.
no code implementations • 12 Jul 2020 • Youngjoon Hong, Bongsuk Kwon, Byung-Jun Yoon
We consider the optimal experimental design problem for an uncertain Kuramoto model, which consists of N interacting oscillators described by coupled ordinary differential equations.