Hyung Ju Hwang | DeepAI

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Featured Co-authors

Jae Yong Lee
11 publications
Taehyun Kim
7 publications
Hwijae Son
6 publications
Cheolhyeong Kim
4 publications
Hyeontae Jo
3 publications
Jin Woo Jang
3 publications
Woo-Jin Han
2 publications
Sung Woong Cho
2 publications
Haeseong Moon
2 publications
Jin Young Shin
2 publications
Rakhoon Hwang
2 publications

∙ 07/05/2022

opPINN: Physics-Informed Neural Network with operator learning to approximate solutions to the Fokker-Planck-Landau equation

We propose a hybrid framework opPINN: physics-informed neural network (P...

0 Jae Yong Lee, et al. ∙

∙ 04/29/2022

AL-PINNs: Augmented Lagrangian relaxation method for Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) has become a prominent applicat...

0 Hwijae Son, et al. ∙

∙ 01/28/2022

Pseudo-Differential Integral Operator for Learning Solution Operators of Partial Differential Equations

Learning mapping between two function spaces has attracted considerable ...

0 Jin Young Shin, et al. ∙

∙ 11/09/2021

Solving PDE-constrained Control Problems using Operator Learning

The modeling and control of complex physical dynamics are essential in r...

0 Rakhoon Hwang, et al. ∙

∙ 09/30/2021

The Deep Minimizing Movement Scheme

Solutions of certain partial differential equations (PDEs) are often rep...

0 Hyung Ju Hwang, et al. ∙

∙ 09/18/2021

Development of patients triage algorithm from nationwide COVID-19 registry data based on machine learning

Prompt severity assessment model of confirmed patients who were infected...

0 Hyung Ju Hwang, et al. ∙

∙ 06/23/2021

Lagrangian dual framework for conservative neural network solutions of kinetic equations

In this paper, we propose a novel conservative formulation for solving k...

0 Hyung Ju Hwang, et al. ∙

∙ 01/22/2021

Prior Preference Learning from Experts:Designing a Reward with Active Inference

Active inference may be defined as Bayesian modeling of a brain with a b...

0 Jinyoung Shin, et al. ∙

∙ 01/22/2021

Sobolev Training for the Neural Network Solutions of PDEs

Approximating the numerical solutions of Partial Differential Equations ...

0 Hwijae Son, et al. ∙

∙ 01/21/2021

Traveling Wave Solutions of Partial Differential Equations via Neural Networks

This paper focuses on how to approximate traveling wave solutions for va...

0 Sung Woong Cho, et al. ∙

∙ 09/28/2020

The model reduction of the Vlasov-Poisson-Fokker-Planck system to the Poisson-Nernst-Planck system via the Deep Neural Network Approach

The model reduction of a mesoscopic kinetic dynamics to a macroscopic co...

0 Jae Yong Lee, et al. ∙

∙ 08/28/2020

Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques

Steemit is a blockchain-based social media platform, where authors can g...

0 Taehyun Kim, et al. ∙

∙ 11/22/2019

Trend to Equilibrium for the Kinetic Fokker-Planck Equation via the Neural Network Approach

The issue of the relaxation to equilibrium has been at the core of the k...

0 Hyung Ju Hwang, et al. ∙

∙ 11/07/2019

Option Compatible Reward Inverse Reinforcement Learning

Reinforcement learning with complex tasks is a challenging problem. Ofte...

0 Rakhoon Hwang, et al. ∙

∙ 09/06/2019

NEAR: Neighborhood Edge AggregatoR for Graph Classification

Learning graph-structured data with graph neural networks (GNNs) has bee...

0 Cheolhyeong Kim, et al. ∙

∙ 07/27/2019

Deep Neural Network Approach to Forward-Inverse Problems

In this paper, we construct approximated solutions of Differential Equat...

0 Hyeontae Jo, et al. ∙

∙ 10/05/2018

Local Stability and Performance of Simple Gradient Penalty mu-Wasserstein GAN

Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance...

4 Cheolhyeong Kim, et al. ∙

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