Uncertainties pervade scientific data, stemming from various sources such as measurement errors, noise, or incomplete information. Overcoming these uncertainties is important for creating reliable model-based predictions and inferences, providing deeper insights into the mathematical models and the physical systems they represent. Therefore, uncertainty quantification (UQ) is essential for robust decision-making in scientific modelling, particularly in fields characterised by complex, large-scale systems. Bayesian inference offers a framework for quantifying uncertainties, yet its application to large-scale simulation … Continue reading ScalaMIDA