176 citations · 189 across the 7 of their papers we have counts for
8 papers
A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware
Eric Müller, Elias Arnold, Oliver Breitwieser +20
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This wor…
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times
Xilu Wang, Yaochu Jin, Sebastian Schmitt +1
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is unten…
Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck +2
Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to add…
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data
Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck +3
Computational Fluid Dynamics (CFD) simulations are a very important tool for many industrial applications, such as aerodynamic optimization of engineering designs like cars shapes,…
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning
Andrea Castellani, Sebastian Schmitt, Stefano Squartini
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologie…
Exploring the fitness landscape of a realistic turbofan rotor blade optimization
Jakub Kmec, Sebastian Schmitt
Aerodynamic shape optimization has established itself as a valuable tool in the engineering design process to achieve highly efficient results. A central aspect for such approaches…