4 papers
Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Sampling
Qitian Lu, Himanshu Sharma, Michael D. Shields +1
Physics-informed polynomial chaos expansions (PC) provide an efficient physically constrained surrogate modeling framework by embedding governing equations and other physical c…
Relationships Between the Maximum Principle and Dynamic Programming for Infinite Dimensional Non-Markovian Stochastic Control Systems
Dingqian Gao, Qi Lü
This paper investigates the relationship between Pontryagin's maximum principle and dynamic programming principle in the context of stochastic optimal control systems governed by s…
Dynamic Programming Principle for Stochastic Control Problems on Riemannian Manifolds
Dingqian Gao, Qi Lü
In this paper, we first establish the dynamic programming principle for stochastic optimal control problems defined on compact Riemannian manifolds without boundary. Subsequently,…
Versatile Differentially Private Learning for General Loss Functions
Qilong Lu, Songxi Chen, Yumou Qiu
This paper aims to provide a versatile privacy-preserving release mechanism along with a unified approach for subsequent parameter estimation and statistical inference. We propose…