ML in cat A safety assessment

This thesis investigates the application of state-of-the-art Python-based machine learning methods to develop metamodels for the NIRAS safety assessment model regarding near-surface disposal of Category A radioactive waste. The role focuses on creating computationally efficient approximations to facilitate global sensitivity analysis and Monte Carlo simulations for uncertainty assessment. It also involves evaluating uncertainty propagation and applying advanced methods to enhance safety-assessment calculations.