5 papers
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…
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…
Physics-Informed Neural Networks for Optimal Vaccination Plan in SIR Epidemic Models
Minseok Kim, Yeongjong Kim, Yeoneung Kim
This work focuses on understanding the minimum eradication time for the controlled Susceptible-Infectious-Recovered (SIR) model in the time-homogeneous setting, where the infection…
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…