activity
20052026
most citedTrend to Equilibrium for the Kinetic Fokker-Planck Equation via the Neural Network Approach

40 citations · 246 across the 34 of their papers we have counts for

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Showing cs.LGShow all

8 papers · 1 filter

cs.LG2023★ 3 cited

HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork

Jae Yong Lee, Sung Woong Cho, Hyung Ju Hwang

Fast and accurate predictions for complex physical dynamics are a significant challenge across various applications. Real-time prediction on resource-constrained hardware is even m…

cs.LG2022★ 4 cited

Enhanced Physics-Informed Neural Networks with Augmented Lagrangian Relaxation Method (AL-PINNs)

Hwijae Son, Sung Woong Cho, Hyung Ju Hwang

Physics-Informed Neural Networks (PINNs) have become a prominent application of deep learning in scientific computation, as they are powerful approximators of solutions to nonlinea…

cs.LG2022★ 1 cited

Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations

Jin Young Shin, Jae Yong Lee, Hyung Ju Hwang

Learning the mapping between two function spaces has garnered considerable research attention. However, learning the solution operator of partial differential equations (PDEs) rema…

cs.LG2021★ 20 cited

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…

cs.LG2021

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

Jin young Shin, Cheolhyeong Kim, Hyung Ju Hwang

Active inference may be defined as Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior…

cs.LG2019

Option Compatible Reward Inverse Reinforcement Learning

Rakhoon Hwang, Hanjin Lee, Hyung Ju Hwang

Reinforcement learning in complex environments is a challenging problem. In particular, the success of reinforcement learning algorithms depends on a well-designed reward function.…