While most current data assimilation algorithms are derived and analyzed from a Bayesian perspective, the CRC will view data assimilation from a general statistical inference perspective. The goal of the proposed CRC is therefore twofold: First, to develop principled methodologies for data assimilation and, second, to demonstrate computational effectiveness and robustness through their implementation for established and novel data assimilation application areas. Furthermore, many new applications are emerging from, for example, biology, medicine, and the cognitive neurosciences, which require novel data assimilation techniques. The field of data assimilation has been largely driven by practitioners from meteorology, hydrology and oil reservoir exploration but a theoretical foundation of the field is largely missing.
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. When the computational model is based on evolutionary equations and the data set is time-ordered, the process of combining models and data is called data assimilation. The seamless integration of large data sets into sophisticated computational models provides one of the central research challenges for the mathematical sciences in the 21st century. CRC 1294/1: Data Assimilation – The Seamless Integration of Data and Models
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