PN 6: Project Overview

PN 6-1	Deep greedy kernel methods for submodel coupling in fluid- and biomechanics
PN 6-2	Surrogate Modelling by Simulation-Enhanced Machine-Learning
PN 6-3	Understanding Physical Constraints in Machine Learning for Simulation
PN 6-4	Visual Analytics for Deep Learning
PN 6-5	Data-integrated Simulation of Human Perception and Cognition
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 6A-1	Investigation of Chemical Reactivity by Machine Learning Techniques
PN 6A-2	Improving the predictability of interfacial phenomena -Description of complex drop-wall interactions in the interaction of experiments, direct numerical simulations and innovative modelling approaches based on artificial intelligence