5 papers
Bifidelity Parameter Estimation Using Conditional Diffusion Models
Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1
We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distr…
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…