40 citations · 68 across the 6 of their papers we have counts for
7 papers
From Nonsmooth Minima to Smooth Branches via Heat Kernel Regularization
Hyeontae Jo
Many optimization problems in science and engineering involve objective functions that are nonsmooth at their minimizers. A common strategy is to trace a branch of minimizers of a…
Learning from Imperfect Data: Robust Inference of Dynamic Systems using Simulation-based Generative Model
Hyunwoo Cho, Hyeontae Jo, Hyung Ju Hwang
System inference for nonlinear dynamic models, represented by ordinary differential equations (ODEs), remains a significant challenge in many fields, particularly when the data are…
Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model
Hyunwoo Cho, Sung Woong Cho, Hyeontae Jo +1
Differential equations (DEs) are crucial for modeling the evolution of natural or engineered systems. Traditionally, the parameters in DEs are adjusted to fit data from system obse…
Estimating the Distribution of Parameters in Differential Equations with Repeated Cross-Sectional Data
Hyeontae Jo, Sung Woong Cho, Hyung Ju Hwang
Differential equations are pivotal in modeling and understanding the dynamics of various systems, offering insights into their future states through parameter estimation fitted to…
Machine Learning-Based COVID-19 Patients Triage Algorithm using Patient-Generated Health Data from Nationwide Multicenter Database
Min Sue Park, Hyeontae Jo, Haeun Lee +2
A prompt severity assessment model of patients confirmed for having infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study…
Trend to Equilibrium for the Kinetic Fokker-Planck Equation via the Neural Network Approach
Hyung Ju Hwang, Jin Woo Jang, Hyeontae Jo +1
The issue of the relaxation to equilibrium has been at the core of the kinetic theory of rarefied gas dynamics. In the paper, we introduce the Deep Neural Network (DNN) approximate…
Deep Neural Network Approach to Forward-Inverse Problems
Hyeontae Jo, Hwijae Son, Hyung Ju Hwang +1
In this paper, we construct approximated solutions of Differential Equations (DEs) using the Deep Neural Network (DNN). Furthermore, we present an architecture that includes the pr…