The solution is widely used for estimating and identifying models for physical systems, like the heat dynamics of a building, the hydraulics in a rainfall run-off system, bacterial growth, pharmacokinetic-pharmacodynamic (PK/PD) models, the dynamics of a heat pump, etc.In this framework the dynamics is described in continuous time, which implies that any physical prior knowledge is easily formulated as a part of the model. CTSM is therefore well suited for modelling both nonlinear and non-stationary systems.Observations are discrete time data (time series), and there are no restriction on the sampling time, which can be either constant or fully varying in time.
Further description and insights into the project is coming soon.