Model calibration - interfaces to combine data, model and inference

Model calibration (or model updating) is a task most scientists are facing when building parameterized models and then updating the parameters based on some experimental data in order to generalize the model and make accurate predictions. For complex models and complex data sets (different experimental tests with different data structures, sensor types and different models) this task is often tedious, difficult to reproduce and often error prone due to complex challenges related to data processing, forward model development and inference. The aim of this roject is to make the process more transparent, easier to setup and work with and more transparent when different people are jointly performing this task.

Contents:

• Motivation