2 citations · 2 across the 4 of their papers we have counts for
5 papers
Error estimates of a training-free diffusion model for high-dimensional sampling
Pengjun Wang, Zezhong Zhang, Minglei Yang +3
Score-based diffusion models are a powerful class of generative models, but their practical use often depends on training neural networks to approximate the score function. Trainin…
Generative AI Models for Learning Flow Maps of Stochastic Dynamical Systems in Bounded Domains
Minglei Yang, Yanfang Liu, Diego del-Castillo-Negrete +2
Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeli…
Numerical approximations for partially observed optimal control of stochastic partial differential equations
Feng Bao, Yanzhao Cao, Hongjiang Qian
In this paper, we study numerical approximations for optimal control of a class of stochastic partial differential equations with partial observations. The system state evolves in…
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
Minglei Yang, Pengjun Wang, Ming Fan +3
We introduce a conditional pseudo-reversible normalizing flow for constructing surrogate models of a physical model polluted by additive noise to efficiently quantify forward and i…
Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation
Yanfang Liu, Minglei Yang, Zezhong Zhang +3
We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks, normalizing flows, va…