Groundbreaking Error Analysis in Bayesian Inverse Problems with Generative AI

This research provides a fascinating look at how data-driven methods, specifically those leveraging generative AI, can enhance solutions to inverse problems. The analysis offers valuable error bounds for models using generative priors, paving the way for more accurate and reliable results. Numerical experiments further validate the practical implications of this innovative approach.

• •Explores the use of generative AI priors in solving inverse problems.
• •Provides quantitative error bounds for minimum Wasserstein-2 generative models.
• •Demonstrates the practical implications through numerical experiments, including an elliptic PDE inverse problem.