2 citations · 3 across the 11 of their papers we have counts for
11 papers
Self-Consistent Adjoint Policy Iteration for Constrained Dynamic Portfolio Choice
Jeonggyu Huh, Yeoneung Kim, Seungwon Jeong
We develop simulation-based policy iteration for continuous-time portfolio choice with predictable returns and convex constraints. Each outer step re-evaluates a fixed-latent open-…
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…
Policy Iteration for Stationary Discounted Hamilton--Jacobi--Bellman Equations: A Viscosity Approach
Namkyeong Cho, Yeoneung Kim
We study policy iteration (PI) for deterministic infinite-horizon discounted control problems characterized by stationary Hamilton--Jacobi--Bellman equations. For general viscosity…
A Physics-Informed, Global-in-Time Neural Particle Method for the Spatially Homogeneous Landau Equation
Minseok Kim, Sung-Jun Son, Yeoneung Kim +1
We propose a physics-informed neural particle method (PINN--PM) for the spatially homogeneous Landau equation. The method adopts a Lagrangian interacting-particle formulation and j…
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 approach for exploratory Hamilton--Jacobi--Bellman equations via policy iterations
Yeongjong Kim, Namkyeong Cho, Minseok Kim +1
We propose a mesh-free policy iteration framework based on physics-informed neural networks (PINNs) for solving entropy-regularized stochastic control problems. The method iterativ…
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…
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…