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

Berkcan Kapusuzoglu, Sankaran Mahadevan

Abstract: Highlights•Physics-informed machine learning is investigated for global sensitivity analysis.•Physics and test data are fused to maximize the accuracy of sensitivity estimates.•Uncertainties in Gaussian process and deep neural network models are included.•Accuracy, uncertainty and computational effort of proposed approaches are compared.