2 citations · 3 across the 11 of their papers we have counts for
5 papers · 1 filter
Monotone Neural Policy Iteration for High-Dimensional First-Order Hamilton--Jacobi--Bellman Equations
Minseok Kim, Yeongjong Kim, Namkyeong Cho +1
We analyze a neural semi-discrete method for high-dimensional first-order Hamilton-Jacobi-Bellman (HJB) equations with known or learned dynamics. Centered differences and an artifi…
Censored Sampling for Topology Design: Guiding Diffusion with Human Preferences
Euihyun Kim, Keun Park, Yeoneung Kim
Recent advances in denoising diffusion models have enabled rapid generation of optimized structures for topology optimization. However, these models often rely on surrogate predict…
Physics-Informed Policy Iteration for High-Dimensional Hamilton--Jacobi--Bellman Equations: Interior Error Bounds without Boundary Data
Yeongjong Kim, Minseok Kim, Yeoneung Kim +1
We develop a physics-informed policy-iteration method for stationary second-order Hamilton--Jacobi--Bellman equations arising in continuous-time stochastic control. Each policy-eva…
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…
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…