Learning Physical Concepts in Cyber-Physical Systems: A Case Study. - Henrik S. Steude; Alexander Windmann; Oliver Niggemann (CoRR, 2021)

Machine Learning for Cyber-Physical Systems. - Oliver Niggemann; Bernd Zimmering; Henrik S. Steude; Jan Lukas Augustin; Alexander Windmann; Samim Ahmad Multaheb (Digital Transformation, 2022)

Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study. - Alexander Windmann; Henrik S. Steude; Oliver Niggemann (CoRR, 2023)

Discret2Di - Deep Learning based Discretization for Model-based Diagnosis. - Lukas Moddemann; Henrik Sebastian Steude; Alexander Diedrich; Oliver Niggemann (CoRR, 2023)

Diagnosis driven Anomaly Detection for CPS. - Henrik S. Steude; Lukas Moddemann; Alexander Diedrich; Jonas Ehrhardt; Oliver Niggemann (CoRR, 2023)

Design Principles for Falsifiable, Replicable and Reproducible Empirical Machine Learning Research. - Daniel Vranjes; Jonas Ehrhardt; René Heesch; Lukas Moddemann; Henrik Sebastian Steude; Oliver Niggemann (DX, 2024)

Using Modular Neural Networks for Anomaly Detection in Cyber-Physical Systems. - Jonas Ehrhardt; Phillip Johann Overlöper; Daniel Vranjes; Henrik S. Steude; Alexander Diedrich; Oliver Niggemann (ETFA, 2024)

Extracting Knowledge using Machine Learning for Anomaly Detection and Root-Cause Diagnosis. - Lukas Moddemann; Henrik Sebastian Steude; Alexander Diedrich; Ingo Pill; Oliver Niggemann (ETFA, 2024)

Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems. - Alexander Windmann; Henrik Sebastian Steude; Daniel Boschmann; Oliver Niggemann (CoRR, 2025)

Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information. - Henrik Sebastian Steude; Alexander Diedrich; Ingo Pill; Lukas Moddemann; Daniel Vranjes; Oliver Niggemann (CoRR, 2025)