PN 6: Project Overview

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)

Project Network Coordinators

This image shows Dirk Pflüger

Dirk Pflüger

Prof. Dr. rer. nat.

[Photo: SimTech/Max Kovalenko]

This image shows Ingo Steinwart

Ingo Steinwart

Univ.-Prof. Dr. rer. nat.

[Photo: SimTech/Max Kovalenko]

Daniel Weiskopf

Prof. Dr.
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