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math.OC2026
Sequential Bayesian Optimal Experimental Design in Infinite Dimensions via Policy Gradient Reinforcement Learning
Kaichen Shen, Peng Chen
Sequential Bayesian optimal experimental design (SBOED) for PDE-governed inverse problems is computationally challenging, especially for infinite-dimensional random field parameter…
math.OC2025
PDPO: Parametric Density Path Optimization
Sebastian Gutierrez Hernandez, Peng Chen, Haomin Zhou
We introduce Parametric Density Path Optimization (PDPO), a novel method for computing action-minimizing paths between probability densities. The core idea is to represent the targ…