From the 1 of 5 linked papers with an AI index.
5 papers
Mesh-dependent iteration count growth in primal-dual active set strategies
Ioannis P. A. Papadopoulos, Michael Hintermüller, Michael Hintermüller
The paper investigates how the number of iterations required by primal‑dual active set methods grows as the mesh is refined for obstacle and Signorini problems, showing linear grow…
Layerwise goal-oriented adaptivity for neural ODEs: an optimal control perspective
Michael Hintermüller, Michael Hintermüller, Michael Hinze +1
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…
Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures
Denis Korolev, Tim Schmidt, Dinesh K. Natarajan +4
This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challe…
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…
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…