Data Assimilation: New Challenges in Random and Stochastic Dynamical Systems

The seamless integration of large data sets into sophisticated computational models provides one of the central challenges for the mathematical sciences in the 21st century. When the computational model is based on dynamical systems, and the data set is time ordered, the process of combining models and data is called data assimilation. The assimilation of data into computational models serves a wide spectrum of purposes ranging from model calibration and model comparison, all the way to the validation of novel model design principles.