2 citations · 3 across the 4 of their papers we have counts for
4 papers
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…
Exact Conditional Score-Guided Generative Modeling for Amortized Inference in Uncertainty Quantification
Zezhong Zhang, Caroline Tatsuoka, Dongbin Xiu +1
We propose an efficient framework for amortized conditional inference by leveraging exact conditional score-guided diffusion models to train a non-reversible neural network as a co…
GenAI4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models
Ming Fan, Zezhong Zhang, Dan Lu +1
We introduce GenAI4UQ, a software package for inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting in scientific applications. Ge…
A Training-Free Conditional Diffusion Model for Learning Stochastic Dynamical Systems
Yanfang Liu, Yuan Chen, Dongbin Xiu +1
This study introduces a training-free conditional diffusion model for learning unknown stochastic differential equations (SDEs) using data. The proposed approach addresses key chal…