| PN 6-1 | Deep greedy kernel methods for submodel coupling in fluid- and biomechanics |
| PN 6-1 (II) | Greedy deep kernel methods for data-based-modelling in Biomechanics |
| PN 6-2 | Surrogate Modelling by Simulation-Enhanced Machine-Learning |
| PN 6-2 (II) | Towards Parameter-Dependent Data-Enriched Physics-Informed Machine Learning |
| PN 6-3 | Understanding Physical Constraints in Machine Learning for Simulation |
| PN 6-3 (II) | Gaussian Process Techniques for Differential Equations |
| PN 6-4 | Visual Analytics for Deep Learning |
| PN 6-4 (II) | Visual Analytics for Machine Learning |
| PN 6-5 | Data-integrated Simulation of Human Perception and Cognition |
| PN 6-5 (II) | Interpretable and explainable cognitive inspired machine learning systems |
| PN 6-6 | Machine Learning for Data-Driven Visualization (ML4Vis) |
| PN 6-7 | Machine Learning for Bayesian Model Building (M4LBMB) |
| PN 6-8 | Visual Data Science to Master Complex Simulation Ensembles |
| PN 6-9 | Analytical models combined with data-driven techniques in dynamic Compton scattering tomography |
| PN 6-10 | Generalization and Robustness of Learned Simulators (GRLSim) |
| PN 6-11 | Data-Integrated Simulation of Interactive Behaviour |
| PN 6-12 | Simulation-Based Prior Distributions for Bayesian Models (SBPriors) |
Associated Projects
Project Network Coordinators
Dirk Pflüger
Prof. Dr. rer. nat.[Photo: SimTech/Max Kovalenko]
Ingo Steinwart
Univ.-Prof. Dr. rer. nat.[Photo: SimTech/Max Kovalenko]