7 papers
Stabilized neural Hamilton--Jacobi--Bellman solvers: Error analysis and applications in model-based reinforcement learning
Minseok Kim, Yeongjong Kim, Namkyeong Cho +1
Physics-informed neural solvers offer a promising route to model-based reinforcement learning in continuous time, where optimal feedback synthesis is governed by Hamilton--Jacobi--…
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 optimal control problems, whose value function is characterized by a stationary Hamilton--Jacobi--Bellm…
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…
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, Yeoneung Kim, Minseok 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…
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…