SFB 1294:  Data Assimilation – The Seamless Integration of Data and Models

• Subject Area: Mathematics
• Term: since 2017
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 318763901

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. 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 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. The field of data assimilation has been largely driven by practitioners from meteorology, hydrology and oil reservoir exploration. However, a theoretical foundation of the field is largely missing. Furthermore, many new applications are emerging from biology, medicine, and cognitive neuroscience, for example. These fields need novel data assimilation techniques. The goal of the CRC is therefore twofold: 1) to develop principled methodologies for data assimilation, and 2) to demonstrate the computational effectiveness and robustness of these methodologies, by implementing them in established and novel application areas. While most current data assimilation algorithms are derived and analysed from a Bayesian perspective, the CRC views data assimilation from a general statistical inference perspective. Major challenges arise from the high dimensionality of the inference problems, the nonlinearity of the models, or non-Gaussian statistics. Targeted application areas include the geosciences (space physics, seismology and hydrology) as well as emerging fields for data assimilation such as biophysics, cognitive neuroscience, and pharmacology.

• DFG Programme: Collaborative Research Centres

Current projects

• A01 - Statistics of stochastic partial differential equations (SPDEs) (Project Heads Beta, Carsten ;  Reiß, Markus ;  Roelly, Sylvie ;  Stannat, Wilhelm )
• A02 - Long-time stability and accuracy of ensemble transform filter algorithms (Project Heads Reich, Sebastian ;  Stannat, Wilhelm ;  De Wiljes, Jana )
• A03 - Sequential and adaptive learning under dependence and non-standard objective functions (Project Heads Blanchard, Gilles ;  Carpentier, Alexandra ;  De Wiljes, Jana )
• A04 - Nonlinear statistical inverse problems with random observations (Project Heads Blanchard, Gilles ;  Freitag, Melina ;  Huisinga, Wilhelm ;  Lie, Han Cheng ;  Reiß, Markus ;  Stankewitz, Bernhard )
• A07 - Data-based model order reduction for stochastic dynamics (Project Heads Freitag, Melina ;  Hartung, Niklas ;  Lie, Han Cheng ;  Redmann, Martin )
• B03 - Gaze dynamics and reinforcement learning during Pavlovian conditioning (Project Heads Carpentier, Alexandra ;  Engbert, Ralf ;  Reich, Sebastian ;  Schad, Daniel )
• B04 - Point process modelling of seismicity: deaggregation and model reduction (Project Heads Hainzl, Sebastian ;  Holschneider, Matthias ;  Reich, Sebastian ;  Zöller, Gert )
• B06 - Reconstructing and predicting the near-Earth radiation environment utilising dimension reduction (Project Heads Freitag, Melina ;  Shprits, Ph.D., Yuri ;  Stolle, Claudia ;  De Wiljes, Jana )
• B07 - Inferring the collective dynamics of active particle by data assimilation (Project Heads Beta, Carsten ;  Großmann, Robert ;  Opper, Manfred )
• B08 - Continual learning by combining reinforcement learning and data assimilation in the context of precision therapy (Project Heads Hartung, Niklas ;  Huisinga, Wilhelm ;  Opper, Manfred ;  De Wiljes, Jana )
• B10 - Bayesian deep learning to study non-Gaussianity, correlations, and change-points in cell-driven transport (Project Heads Beta, Carsten ;  Metzler, Ralf )
• B11 - Understanding time-varying Earth System properties using data assimilation (Project Heads Reich, Sebastian ;  Wagener, Thorsten )
• MGK - Integrated Research Training Group (Project Heads Freitag, Melina ;  Huisinga, Wilhelm ;  Roelly, Sylvie )
• Z01 - Central tasks of the CRC (Project Heads Freitag, Melina ;  Reich, Sebastian )

Completed projects

• A05 - Combining nonparametric statistical and probabilistic approaches for inference on cloud-of-points data (Project Heads Blanchard, Gilles ;  Roelly, Sylvie )
• A06 - Approximate Bayesian inference and model selection for stochastic differential equations (SDEs) (Project Heads Opper, Manfred ;  Reich, Sebastian ;  Spokoiny, Vladimir )
• B02 - Inferring the dynamics underlying protrusion-driven cell motility (Project Heads Beta, Carsten ;  Holschneider, Matthias ;  Huisinga, Wilhelm )
• B05 - Attention selection and recognition in scene viewing (Project Heads Engbert, Ralf ;  Scheffer, Tobias )
• B09 - Neural network modelling of brain responses during language comprehension (Project Heads Rabovsky, Milena ;  Reich, Sebastian )
• INF - Information infrastructure for data assimilation (Project Heads Engbert, Ralf ;  Lucke, Ulrike ;  Scheffer, Tobias )

• Applicant Institution: Universität Potsdam
• Participating University: Humboldt-Universität zu Berlin; Technische Universität Berlin; Technische Universität Ilmenau; Universität Rostock
• Participating Institution: GFZ Helmholtz-Zentrum für Geoforschung; HMU Research, Development und Innovation gGmbH (HMU RDI)
• Spokespersons: Professorin Dr. Melina Freitag, since 1/2025; Professor Dr.-Ing. Sebastian Reich, until 12/2024