12 citations · 31 across the 5 of their papers we have counts for
5 papers
Going Deeper with Five-point Stencil Convolutions for Reaction-Diffusion Equations
Yongho Kim, Yongho Choi
Physics-informed neural networks have been widely applied to partial differential equations with great success because the physics-informed loss essentially requires no observation…
Convolutional Autoencoders, Clustering and POD for Low-dimensional Parametrization of Navier-Stokes Equations
Yongho Kim, Jan Heiland
Simulations of large-scale dynamical systems require expensive computations. Low-dimensional parametrization of high-dimensional states such as Proper Orthogonal Decomposition (POD…
Learning finite difference methods for reaction-diffusion type equations with FCNN
Yongho Kim, Yongho Choi
In recent years, Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations alongside numerical methods because PINNs can be trained wit…
Disturbing Target Values for Neural Network Regularization
Yongho Kim, Hanna Lukashonak, Paweena Tarepakdee +2
Diverse regularization techniques have been developed such as L2 regularization, Dropout, DisturbLabel (DL) to prevent overfitting. DL, a newcomer on the scene, regularizes the los…
Fast Numerical Simulation of Allen-Cahn Equation
Yongho Kim, Yongho Choi
Simulation speed depends on code structures, hence it is crucial how to build a fast algorithm. We solve the Allen-Cahn equation by an explicit finite difference method, so it requ…