PN 1: Data-Integrated Models and Methods for Multiphase Fluid Dynamics will advance models and simulation methods for multiphase processes in turbulent, free, and porous media flow. The goal is to overcome previously unsolvable challenges in multi-X simulations by integrating experimental data, in particular to cover wide ranges of scales and flow regimes.
PN 2: In Silico Models of Coupled Biological Systems focuses on holistic yet person-specific computational models of, for example, the neuromuscular system. The key questions revolve around system models, knowledge-based and data-driven coupling, individualization, data and model standards, and resource-limited simulations.
PN 3: Data-Integrated Model Reduction for Particles and Continua will develop new techniques for data integration into models of materials and biological matter. The predictive power of reduced or coarse-grained models will be enhanced by incorporating data from different sources, like experiments, simulations, or rapidly growing public databases.
PN 4: Data-Integrated Control System Design with Guarantees will develop novel methods to control individual systems or networks of systems. It will exploit the benefit of data and learning strategies on top of classical first-principles models while still providing rigorous guarantees for the overall system behavior in all steps of the systems and control design cycle.
PN 6: Machine Learning for Simulation will integrate the so far separated fields of classical simulations and machine learning, paving the way to joint model-based and data-driven predictions. Assisted by novel visualization techniques, it also explores how physical models and simulations can improve machine learning and vice versa.
PN 7: Adaptive Simulation and Interaction is key to pervasive simulations in dynamically changing heterogeneous communication and computing infrastructures. It focusses on modeling and real-time adaptation of systems, traceability and provenance, adaptive user interaction, and visualization.