3 papers
cs.LG2025
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…
cs.LG2024
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…
cs.LG2024
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) --…