Current research at SimTech investigates how simulation methods can be extended beyond their current limits. Key questions include how uncertainty can be represented and quantified more realistically, how machine learning can be integrated into simulation while preserving physical and mathematical properties, and how complex, high-dimensional models can be reduced without losing essential information. Researchers also explore how information can be transferred across scales, how experimental and simulation data can be combined to improve models and predictions, and how complex simulation results can be made more accessible and understandable through new forms of interaction and visualization. Together, these approaches aim to make simulations more reliable, efficient and informative across a broad range of scientific and engineering applications.
| Project Titel | Project Lead |
| A dual-scale sub-grid closure for compressible LES of phase interfaces with phase change | Prof. Dr.-Ing. Andrea Beck |
| Effective closures and two-scale approaches for compressible two-phase flow on an averaged scale | Prof. Dr. Christian Rohde |
| DNS of droplet-wall-interaction using structured wall surfaces | Prof. Dr.-Ing. habil. Bernhard Weigand |
| Smoothed Particle Hydrodynamics for near-field investigations of multiphase particle-laden flows | Prof. Dr.-Ing. Holger Steeb |
| Asymptotic equations for rimming flows: modeling, analysis and simulations | Jun.-Prof. Dr. Christina Lienstromberg |
| Certified model order reduction for eigenvalue clusters of large-scale Hermitian matrices | Prof. Dr. Benjamin Stamm |
| Machine learning techniques for model reduction | Univ.-Prof. Dr. rer. nat. Ingo Steinwart |
| Analytical and data-driven derivation of effective and reduced-dimensional models for compressible flow in porous media | Prof. Dr. Christian Rohde |
| Learning Failure of Lightweight Technical Components | Prof. Dr.-Ing. Jörg Fehr |
| Novel Approaches for Learning PD Kernels | Prof. Dr.-Ing. Felix Fritzen |
| Data-based Symplectic Numerical Integrators by Learning Hamiltonian Flow Maps | Prof. Dr. Bernard Haasdonk |
| Machine-learned Interatomic Interactions under Physical Constraints | Prof. Dr. Johannes Kästner |
| Generative AI for Physics-Aware Surrogates with Guarantees | Prof. Dr. Mathias Niepert |
| Learning Physics-informed Models in Reduced Spaces | Prof. Dr. Dirk Pflüger |
| Generative AI for Learning Nonlinear Dynamic Systems with Calibrated Uncertainties | Prof. Dr. Steffen Staab |
| Statistical Inference of Cancer Drug Tolerance | Jun.-Prof. Dr. Chengzhe Tian |
| Adaptive prediction methods for uncertainties and errors in geoscience simulations | Prof. Dr. Miriam Schulte |
| New frameworks for inverse uncertainty propagation in a systems biology context | Prof. Dr. rer. nat. Nicole Radde |
| Bayesian Inference for Extremes of Latent Processes | Prof. Dr. rer. nat. Marco Oesting |
| Bayesian Geostatistics for non-Stationary, non-Gaussian Random Functions | Prof. Dr.-Ing. Wolfgang Nowak |
| Bayesian treatment of model inexactness in dynamic inverse problems | Prof. Dr. Bernadette Hahn-Rigaud |
| Knowledge Infused Bayesian Optimization of Hyperparameters in Parameter Studies | Prof. Dr. rer. nat. Dominik Göddeke |
| Bayesian inference for stochastic partial differential equations | Prof. Dr. Andrea Barth |
| Bayesian Inference for Stochastic Models: Generalized Methods for Forward and Backward Uncertainty Quantification (Bayes 2.0) | Prof. Dr. Heng Xiao |
| Coupled in-situ rheology for multifunctional materials and modelling of coupled processes | Prof. Dr.-Ing. Holger Steeb |
| Multifunctional materials design and hierarchical architectures for tailor-made wetting and mechanical properties | Prof. Dr. rer. nat. habil. Sabine Ludwigs |
| Modelling interfacial properties of polyelectrolyte and solvent systems with external electric fields and approaches to design of multifunctional | Prof. Dr.-Ing. Joachim Gross |
| Information infrastructure for multifunctional materials | Prof. Dr.-Ing. Bernd Flemisch |
| Numerical Model Coupling | Prof. Dr. Miriam Schulte |
| Visualization and Tangible Interaction for Collaborative Simulation | Prof. Dr. Daniel Weiskopf |
| Embodied User Interfaces for Simulation Storytelling | Prof. Dr. Michael Sedlmair |
| Neuromechanical Simulation for Immersion in Extended Reality | Prof. Dr. Syn Schmitt |
| CAMMP-Projekt | Prof. Dr. Benjamin Stamm |
| Reservoir computing utilizing many-body dynamics (of swarm models) | Dr. Miriam Klopotek |
| Thought experiments combined with physics simulations and machine learning in a human-AI learning context | Dr. Miriam Klopotek |
| Biological molecular dynamics simulations 2.0 | Dr. Kristyna Pluhackova |
| Artificial Intelligence meets Molecular Dynamics | Dr. Kristyna Pluhackova |
| Bayesian Inference for Stochastic Models: Generalized Methods for Forward and Backward Uncertainty Quantification (Bayes 2.0) | Dr. Andrea Iannelli |
| GeoMod4Future: Zukunftsfähige Modellierung für Geowissenschaften | Dr.Anneli Guthke |