6 citations · 7 across the 6 of their papers we have counts for
6 papers
Learning Hamiltonian Dynamics with Bayesian Data Assimilation
Taehyeun Kim, Tae-Geun Kim, Anouck Girard +1
In this paper, we develop a neural network-based approach for time-series prediction in unknown Hamiltonian dynamical systems. Our approach leverages a surrogate model and learns t…
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?
Tae-Geun Kim, Seong Chan Park
We propose a novel framework based on neural network that reformulates classical mechanics as an operator learning problem. A machine directly maps a potential function to its corr…
HyperbolicLR: Epoch insensitive learning rate scheduler
Tae-Geun Kim
This study proposes two novel learning rate schedulers -- Hyperbolic Learning Rate Scheduler (HyperbolicLR) and Exponential Hyperbolic Learning Rate Scheduler (ExpHyperbolicLR) --…
Unsupervised sequence-to-sequence learning for automatic signal quality assessment in multi-channel electrical impedance-based hemodynamic monitoring
Chang Min Hyun, Tae-Geun Kim, Kyounghun Lee
This study proposes an unsupervised sequence-to-sequence learning approach that automatically assesses the motion-induced reliability degradation of the cardiac volume signal (CVS)…
DeeLeMa: Missing information search with Deep Learning for Mass estimation
Kayoung Ban, Dong Woo Kang, Tae-Geun Kim +2
We introduce DeeLeMa, a deep learning-based network for the analysis of energy and momentum in high-energy particle collisions. This novel approach is specifically designed to addr…
Primordial Black Holes as a Factory of Axions: Extragalactic Photons from Axions
Yongsoo Jho, Tae-Geun Kim, Jong-Chul Park +2
Primordial black holes (PBHs) are significant sources of axions and axion-like particles (ALPs), provided their Hawking temperature exceeds the particles' masses. Given the predomi…