A Python package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting

Area: Artificial intelligence

CASS member: LEADS

Description

GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency, as well as a versatile hyperparameter auto-tuning framework.

Target audience

Science teams interested in uncertainty quantification and inverse modeling.

License: MIT

Additional resources