Gaussian Process Approximation for Differential Equations
This thesis develops a Bayesian framework based on Gaussian processes for inferring parameters of differential operators from data and reconstructing the corresponding solution together with its uncertainty. Such inverse problems are often ill-posed and sensitive to noise, which makes classical deterministic inverse approaches unstable and limits their ability to quantify uncertainty. The setup co
