Extending Variational Inference (VI), an approximate bayesian inversion method, to hybrid models, i.e. models that combine mechanistic and machine-learning parts.
The model inversion, infers parametric approximations of posterior density of model parameters, by comparing model outputs to uncertain observations. At the same time, a machine learning model is fit that predicts parameters of these approximations by covariates.