39 citations · 39 across the 6 of their papers we have counts for
5 papers · 1 filter
Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept
Christian Wittke, Stephan Myschik, Oliver Niggemann
We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control. For a planar X8 coaxial multicopter, we learn $p(u \m…
Benchmarking Sensor-Fault Robustness in Forecasting
Alexander Windmann, Philipp Wittenberg, Gianluca Manca +3
Cyber-physical system (CPS) forecasting models depend on sensor streams with noisy, biased, missing, or temporally misaligned readings, yet standard forecasting evaluation often se…
MAWIFlow Benchmark: Realistic Flow-Based Evaluation for Network Intrusion Detection
Joshua Schraven, Alexander Windmann, Oliver Niggemann
Benchmark datasets for network intrusion detection commonly rely on synthetically generated traffic, which fails to reflect the statistical variability and temporal drift encounter…
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems
Alexander Windmann, Henrik Steude, Daniel Boschmann +1
Cyber-Physical Systems (CPS) in domains such as manufacturing and energy distribution generate complex time series data crucial for Prognostics and Health Management (PHM). While D…
Design Principles for Falsifiable, Replicable and Reproducible Empirical ML Research
Daniel Vranješ, Oliver Niggemann
Empirical research plays a fundamental role in the machine learning domain. At the heart of impactful empirical research lies the development of clear research hypotheses, which th…