This study focuses on parameter inference in a pulmonary blood cir- culation model for mice. It utilises a fluid dynamics network model that takes selected parameter values and aims to mimic features of the pulmonary haemody- namics under normal physiological and pathological conditions. This is of medical relevance as it allows monitoring of the progression of pulmonary hypertension. Constraint nonlinear optimization is successfully used to learn the parameter values
