- 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 Design of Functional Matter Across Scales
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 5: On-the-fly Model Modification, Error Control, and Simulation Adaptivity
will balance systematic errors and stochastic variations against resource limitations in computing power and available data. The combination with experimental and metadata will enable large-scale simulations of unprecedented predictive power and performance.
- 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.
- PN 8: Accelerating Simulation by Quantum Computing
is of range for complete large-scale simulations in the current NISQ (noisy intermediate scale) phase of quantum computing. However, recent results show that quantum systems might require fundamentally new numerical methods based on inner building blocks that run very efficiently on quantum computers while being very inefficient on standard hardware. This explorative PN aims to open this perspective for revolutionary new algorithms in SimTech.
- International Projects
- Transfer Projects
- Third-party Funding Projects
- PostDoc Projects