2 citations · 3 across the 11 of their papers we have counts for
4 papers · 1 filter
Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control
Yejun Choi, Yeoneung Kim, Keun Park
Metamaterial mechanisms are micro-architectured compliant structures that operate through the elastic deformation of specially designed flexible members. This study develops an eff…
Hamilton-Jacobi Based Policy-Iteration via Deep Operator Learning
Jae Yong Lee, Yeoneung Kim
The framework of deep operator network (DeepONet) has been widely exploited thanks to its capability of solving high dimensional partial differential equations. In this paper, we i…
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation
Yeachan Park, Minseok Kim, Yeoneung Kim
We propose novel methodologies aimed at accelerating the grokking phenomenon, which refers to the rapid increment of test accuracy after a long period of overfitting as reported in…
On the stability of Lipschitz continuous control problems and its application to reinforcement learning
Namkyeong Cho, Yeoneung Kim
We address the crucial yet underexplored stability properties of the Hamilton--Jacobi--Bellman (HJB) equation in model-free reinforcement learning contexts, specifically for Lipsch…