Accurate uncertainty quantification for model-based, large-scale inverse problems represents one of the fundamental challenges in the context of computational science and engineering. In this thesis, novel Bayesian methodologies for the quantification of parametric and model uncertainties are proposed. Their performance is demonstrated on problems in elastography where the identification of the mechanical properties of biological materials can significantly enhance non-invasive, medical diagnosis.     «

Accurate uncertainty quantification for model-based, large-scale inverse problems represents one of the fundamental challenges in the context of computational science and engineering. In this thesis, novel Bayesian methodologies for the quantification of parametric and model uncertainties are proposed. Their performance is demonstrated on problems in elastography where the identification of the mechanical properties of biological materials can significantly enhance non-invasive, medical diagnosi...     »