Projects
Project group A: Data Processing
A01: Gradient descent for deep neural network learning
PIs: H. Rauhut, M. Westdickenberg
A02: Scattering transforms of sparse signals
PI: H. Führ
A03: Group actions and t-designs in sparse and low rank matrix recovery
PIs: H. Führ, G. Nebe, H. Rauhut
A06: Theta tensor norms and low rank recovery
PIs: G. Fourier, H. Rauhut
A07: Signal processing on graphs and complexes
PI: M. Schaub
A08: Sparse exit wave reconstruction via deep unfolding
PI: B. Berkels
A09: Regularizing neural network classification using random perturbations
PIs: S. Krumscheid, H. Rauhut, R. Tempone
Project group B: Kinetic and Parametric Models
B01: Nonlinear reduced modeling for state and parameter estimation
PIs: M. Bachmayr, W. Dahmen
B02: Robust sparse low rank approximation of multi-parametric partial differential equations
PIs: M. Bachmayr, L. Grasedyck
B03: Robust data-driven coarse-graining for surrogate modeling
PI: S. Krumscheid
B04: Sparsity promoting patterns in kinetic hierarchies
PIs: M. Herty, M. Torrilhon
B05: Sparsification of time-dependent network flow problems by discrete optimization
PIs: C. Büsing, M. Herty, A. Koster
B06: Kinetic theory meets algebraic systems theory
PIs: M. Herty, E. Zerz
Project group C: Singular and geometric PDEs
C01: Singularity formation in dissipative harmonic flows
PIs: C. Melcher, A. Reusken
C02: Intrinsic convexity in the Mullins-Sekerka evolution
PIs: M. Westdickenberg, M.G. Westdickenberg
PIs: A. Reusken, B. Stamm
C05: Numerical approximation of the Gross-Pitaevskii equation via vortex tracking
PIs: C. Melcher, B. Stamm