One of the challenges modellers face in biological sciences is to calibrate models in order to match as closely as possible observations and gain predictive power. Scientific machine learning addresses this problem by applying optimisation techniques originally developed within the field of machine learning to mechanistic models, allowing to infer parameters directly from observation data.
