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)
PN 6-13	Science-Constrained Learning
PN 6-14	Resource-efficient Domain Adaptation and Simulation-Enhanced Learning in Spatiotemporal Data

PN 6A-1	Investigation of Chemical Reactivity by Machine Learning Techniques
PN 6A-2	Improving the predictability of interfacial phenomena
PN 6A-3	Advanced learning strategies for potential energy surfaces applied to organic electrolytes
PN 6A-4	Unified diagnostic evaluation of physics-based, data-driven and hybrid hydrological models based on information theory