2 citations · 4 across the 5 of their papers we have counts for
8 papers
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…
Robust Entropy Search for Safe Efficient Bayesian Optimization
Dorina Weichert, Alexander Kister, Sebastian Houben +2
The practical use of Bayesian Optimization (BO) in engineering applications imposes special requirements: high sampling efficiency on the one hand and finding a robust solution on…
Robustness in Fatigue Strength Estimation
Dorina Weichert, Alexander Kister, Sebastian Houben +2
Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In th…
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs
Nathalie Paul, Tim Wirtz, Stefan Wrobel +1
We interpret solving the multi-vehicle routing problem as a team Markov game with partially observable costs. For a given set of customers to serve, the playing agents (vehicles) h…
Bayesian Optimization for Min Max Optimization
Dorina Weichert, Alexander Kister
A solution that is only reliable under favourable conditions is hardly a safe solution. Min Max Optimization is an approach that returns optima that are robust against worst case c…
Monte Carlo Tree Search for high precision manufacturing
Dorina Weichert, Felix Horchler, Alexander Kister +3
Monte Carlo Tree Search (MCTS) has shown its strength for a lot of deterministic and stochastic examples, but literature lacks reports of applications to real world industrial proc…
Approaching Neural Network Uncertainty Realism
Joachim Sicking, Alexander Kister, Matthias Fahrland +5
Statistical models are inherently uncertain. Quantifying or at least upper-bounding their uncertainties is vital for safety-critical systems such as autonomous vehicles. While stan…
Sample path large deviations for Laplacian models in -dimensions
Stefan Adams, Alexander Kister, Hendrik Weber
For Laplacian models in dimension we derive sample path large deviations for the profile height function, that is, we study scaling limits of Gaussian integrated random wal…