Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data

Summary

This paper studies global sensitivity analysis when both physics-based models and experimental observations are available.

Research contribution

The work evaluates physics-informed strategies for combining those information sources with deep neural networks and Gaussian-process models.