27 citations · 75 across the 17 of their papers we have counts for
17 papers
Numerical Analysis of HiPPO-LegS ODE for Deep State Space Models
Jaesung R. Park, Jaewook J. Suh, Youngjoon Hong +1
In deep learning, the recently introduced state space models utilize HiPPO (High-order Polynomial Projection Operators) memory units to approximate continuous-time trajectories of…
An Adaptive Dual-level Reinforcement Learning Approach for Optimal Trade Execution
Soohan Kim, Jimyeong Kim, Hong Kee Sul +1
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. Previous studies o…
Separable Physics-Informed Neural Networks
Junwoo Cho, Seungtae Nam, Hyunmo Yang +3
Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental lim…
Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs
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 different…
Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
Junwoo Cho, Seungtae Nam, Hyunmo Yang +3
Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forward and inverse problems. While promising, the expensive computational costs to ob…
Invertible Monotone Operators for Normalizing Flows
Byeongkeun Ahn, Chiyoon Kim, Youngjoon Hong +1
Normalizing flows model probability distributions by learning invertible transformations that transfer a simple distribution into complex distributions. Since the architecture of R…
Physics-Informed Convolutional Transformer for Predicting Volatility Surface
Soohan Kim, Seok-Bae Yun, Hyeong-Ohk Bae +2
Predicting volatility is important for asset predicting, option pricing and hedging strategies because it cannot be directly observed in the financial market. The Black-Scholes opt…
Semi-analytic PINN methods for singularly perturbed boundary value problems
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. The PINN is a scientific machine learning framework tha…
PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers
Namgyu Kang, Byeonghyeon Lee, Youngjoon Hong +2
With the increases in computational power and advances in machine learning, data-driven learning-based methods have gained significant attention in solving PDEs. Physics-informed n…
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations
Junho Choi, Namjung Kim, Youngjoon Hong
Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems. These approaches have been developed into a novel research fiel…
Accelerating Optimal Experimental Design for Robust Synchronization of Uncertain Kuramoto Oscillator Model Using Machine Learning
Hyun-Myung Woo, Youngjoon Hong, Bongsuk Kwon +1
Recent advances in objective-based uncertainty quantification (objective-UQ) have shown that such a goal-driven approach for quantifying model uncertainty is extremely useful in re…
Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation
Bryce Chudomelka, Youngjoon Hong, Hyunwoo Kim +1
Nonlinear differential equations are challenging to solve numerically and are important to understanding the dynamics of many physical systems. Deep neural networks have been appli…
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods
Byungjoo Kim, Bryce Chudomelka, Jinyoung Park +3
Deep neural networks have achieved state-of-the-art performance in a variety of fields. Recent works observe that a class of widely used neural networks can be viewed as the Euler…
Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations
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 equation…
Timestepping schemes for the 3d Navier-Stokes equations: small solutions and short times
Youngjoon Hong, Djoko Wirosoetisno
It is well known that the solution of the 3d Navier--Stokes equations remains bounded if the initial data and the forcing are sufficiently small relative to the viscosity, and for…
Numerical weather prediction in two dimensions with topography, using a finite volume method
Arthur Bousquet, Mickaël D. Chekroun, Youngjoon Hong +2
We aim to study a finite volume scheme to solve the two dimensional inviscid primitive equations of the atmosphere with humidity and saturation, in presence of topography and subje…
Numerical approximation of the singularly perturbed heat equation in a circle
Youngjoon Hong
In this article we study the two dimensional singularly perturbed heat equation in a circular domain. The aim is to develop a numerical method with a uniform mesh, avoiding mesh re…