• Bayesian uncertainty quantification for machine-learned models in physics 

      Gal Y., Koumoutsakos P., Lanusse F., Louppe G., Papadimitriou C. (2022)
      Being able to quantify uncertainty when comparing a theoretical or computational model to observations is critical to conducting a sound scientific investigation. With the rise of data-driven modelling, understanding various ...
    • Hierarchical bayesian model updating for probabilistic damage identification 

      Behmanesh I., Moaveni B., Lombaert G., Papadimitriou C. (2015)
      This paper presents the newly developed Hierarchical Bayesian model updating method for identification of civil structures. The proposed updating method is suitable for uncertainty quantification of model updating parameters, ...