• 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 ...