1 citations · 1 across the 2 of their papers we have counts for
5 papers
FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation
Jae Yong Lee, Gwang Jae Jung, Byung Chan Lim +1
The Boltzmann equation, a fundamental model in kinetic theory, describes the evolution of particle distribution functions through a nonlinear, high-dimensional collision operator.…
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…
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids
Sung Woong Cho, Jae Yong Lee, Hyung Ju Hwang
Scientific computing using deep learning has seen significant advancements in recent years. There has been growing interest in models that learn the operator from the parameters of…