Adaptive Design of Experiments to Fit Surrogate Gaussian Process Regression Models Allows Fast Sensitivity Analysis of the Input Waveform for Patient-Specific 3D CFD Models of Liver Radioembolization

Patient-specific 3D computational fluid dynamics (CFD) models are increasingly being used to understand and predict transarterial radioembolization procedures used for hepatocellular carcinoma treatment. While sensitivity analyses of these CFD models can help to determine the most impactful input parameters, such analyses are computationally costly. Therefore, we aim to use surrogate modelling to allow relatively cheap sensitivity analysis. As an example, we compute Sobol's sensitivity indices for three input waveform shape parameters.