GenAI4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models
This provides a faster, user-friendly tool for researchers and practitioners in scientific domains to improve uncertainty quantification in model calibration and forecasting, though it appears incremental as it builds on existing generative AI methods.
The authors tackled the problem of computationally intensive inverse uncertainty quantification in scientific modeling by developing GenAI4UQ, a software package that uses conditional generative AI to replace traditional methods like Markov Chain Monte Carlo, resulting in efficient calibration, ensemble forecasting, and robust uncertainty quantification.
We introduce GenAI4UQ, a software package for inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting in scientific applications. GenAI4UQ leverages a generative artificial intelligence (AI) based conditional modeling framework to address the limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of model input parameters and generation of output predictions directly from observations. The software's design allows for rapid ensemble forecasting with robust uncertainty quantification, while maintaining high computational and storage efficiency. GenAI4UQ simplifies the model training process through built-in auto-tuning of hyperparameters, making it accessible to users with varying levels of expertise. Its conditional generative framework ensures versatility, enabling applicability across a wide range of scientific domains. At its core, GenAI4UQ transforms the paradigm of inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling. (The code and data are available at https://github.com/patrickfan/GenAI4UQ).