3 citations · 3 across the 8 of their papers we have counts for
8 papers
Drift-Free Conservative Dynamics from Quantized Interaction Rules
Park Junhu, Youngsoo Ha, Myungjoo Kang
Conservation laws are conventionally discretized through floating-point flux evaluation, with invariants obtained by cancellation of approximate interface contributions and admissi…
Continuum dynamics from quantised interaction rules
Park Junhu, Youngsoo Ha, Myungjoo Kang
Hyperbolic conservation laws are conventionally solved by evolving reconstructed floating-point fields, incurring both computational overhead and structural diffusion near disconti…
Divergence-Based Similarity Function for Multi-View Contrastive Learning
Jaehyoung Jeon, Cheolsu Lim, Myungjoo Kang
Recent success in contrastive learning has sparked growing interest in more effectively leveraging multiple augmented views of data. While prior methods incorporate multiple views…
Residual Connection-Enhanced ConvLSTM for Lithium Dendrite Growth Prediction
Hosung Lee, Byeongoh Hwang, Dasan Kim +1
The growth of lithium dendrites significantly impacts the performance and safety of rechargeable batteries, leading to short circuits and capacity degradation. This study proposes…
Decomposition of one-layer neural networks via the infinite sum of reproducing kernel Banach spaces
Seungcheol Shin, Myungjoo Kang
In this paper, we define the sum of RKBSs using the characterization theorem of RKBSs and show that the sum of RKBSs is compatible with the direct sum of feature spaces. Moreover,…
Why Rectified Power Unit Networks Fail and How to Improve It: An Effective Field Theory Perspective
Taeyoung Kim, Myungjoo Kang
The Rectified Power Unit (RePU) activation function, a differentiable generalization of the Rectified Linear Unit (ReLU), has shown promise in constructing neural networks due to i…
Neural Operators Learn the Local Physics of Magnetohydrodynamics
Taeyoung Kim, Youngsoo Ha, Myungjoo Kang
Magnetohydrodynamics (MHD) plays a pivotal role in describing the dynamics of plasma and conductive fluids, essential for understanding phenomena such as the structure and evolutio…
Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Laws
Taeyoung Kim, Myungjoo Kang
Traditionally, classical numerical schemes have been employed to solve partial differential equations (PDEs) using computational methods. Recently, neural network-based methods hav…