Managing the response to natural disasters effectively can considerably
...
Optimizing building configurations for an efficient use of energy is
inc...
The BrainScaleS-2 (BSS-2) system implements physical models of neurons a...
The first-generation of BrainScaleS, also referred to as BrainScaleS-1, ...
Identifying meaningful concepts in large data sets can provide valuable
...
Multi-objective optimization problems whose objectives have different
ev...
Bayesian optimization has emerged at the forefront of expensive black-bo...
Data stream classification is an important problem in the field of machi...
Neuromorphic systems open up opportunities to enlarge the explorative sp...
An important issue during an engineering design process is to develop an...
Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly a...
Detecting drifts in data is essential for machine learning applications,...
Selecting a minimal feature set that is maximally informative about a ta...
In complex industrial settings, it is common practice to monitor the
ope...
Convolutional neural network training can suffer from diverse issues lik...
Computational Fluid Dynamics (CFD) simulations are a very important tool...
In the context of optimization approaches to engineering applications,
t...
The continuously growing amount of monitored data in the Industry 4.0 co...
We present software facilitating the usage of the BrainScaleS-2 analog
n...
We present software facilitating the usage of the BrainScaleS-2 analog
n...
BrainScaleS-1 is a wafer-scale mixed-signal accelerated neuromorphic sys...
Aerodynamic shape optimization has established itself as a valuable tool...
We investigate the trade-off between the robustness against random and
t...
The traditional von Neumann computer architecture faces serious obstacle...
Despite being originally inspired by the central nervous system, artific...
Emulating spiking neural networks on analog neuromorphic hardware offers...