Advanced Numerics for Uncertainty and Bayesian Inference in Science

Scientific knowledge enters computers through data on the one side, and laws of nature – implicit equations like differential equations and symmetries – on the other. They both provide information, empirical and mechanistic, respectively, crucial to the deduction of new insights. But the algorithms that operate on these sources of information stem from different communities and different eras: machine learning – ""big data"" – on the one hand, and simulation methods – high performance computing – on the other. One of the problems that arise from this disconnect is that inferring latent forces that drive dynamical systems from data requires ""shoehorning"" different algorithms together in inefficient optimization loops