5 papers · 1 filter
Mesh-dependent iteration count growth in primal-dual active set strategies
Ioannis P. A. Papadopoulos, Michael Hintermüller
Primal-dual active set strategies (PDAS) are popular iterative solvers for mixed complementarity problems such as constrained optimization problems with pointwise inequality constr…
Layerwise goal-oriented adaptivity for neural ODEs: an optimal control perspective
Michael Hintermüller, Michael Hinze, Denis Korolev
In this work, we propose a novel layerwise adaptive construction method for neural network architectures. Our approach is based on a goal--oriented dual-weighted residual technique…
A neural network approach to learning solutions of a class of elliptic variational inequalities
Amal Alphonse, Michael Hintermüller, Alexander Kister +2
We develop a weak adversarial approach to solving obstacle problems using neural networks. By employing (generalised) regularised gap functions and their properties we rewrite the…
Data-driven methods for quantitative imaging
Guozhi Dong, Moritz Flaschel, Michael Hintermüller +3
In the field of quantitative imaging, the image information at a pixel or voxel in an underlying domain entails crucial information about the imaged matter. This is particularly im…
Minimal and maximal solution maps of elliptic QVIs of obstacle type: Lipschitz stability, differentiability and optimal control
Amal Alphonse, Michael Hintermüller, Carlos N. Rautenberg +1
Quasi-variational inequalities (QVIs) of obstacle type in many cases have multiple solutions that can be ordered. We study a multitude of properties of the operator mapping the sou…