5 papers
A Sample-Wise Adjoint Regression Framework for Mean-Field Control with Connections to Adjoint Matching
Hui Sun
This work proposes a novel numerical approach for solving mean-field control (MFC) problems using an adjoint-based optimization framework motivated by the stochastic maximum princi…
The Ensemble Schr{ö}dinger Bridge filter for Nonlinear Data Assimilation
Hui Sun
This work introduces a novel nonlinear optimal filtering method, termed the Ensemble Schr{ö}dinger Bridge nonlinear filter. The proposed filter combines the standard prediction ste…
Batch Sample-wise Stochastic Optimal Control via Stochastic Maximum Principle
Hui Sun, Feng Bao
In this work, we study the stochastic optimal control problem (SOC) mainly from the probabilistic view point, i.e. via the Stochastic Maximum principle (SMP) \cite{Peng4}. We adopt…
Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm
Siming Liang, Hui Sun, Richard Archibald +1
This paper presents convergence analysis of a novel data-driven feedback control algorithm designed for generating online controls based on partial noisy observational data. The al…
Solving high dimensional FBSDE with deep signature techniques with application to nonlinear options pricing
Hui Sun, Feng Bao
We report two methods for solving FBSDEs of path dependent types of high dimensions. Specifically, we propose a deep learning framework for solving such problems using path signatu…