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ORCIDPhilipp Maximilian Sieberg
Person information
- affiliation: University of Duisburg-Essen, Faculty of Engineering, Germany
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2020 – today
- 2022
[j2]Philipp Maximilian Sieberg
, Dieter Schramm:
Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems. Sensors 22(9): 3513 (2022)- [j1]Philipp Maximilian Sieberg, Sebastian Blume, Sebastian Reicherts, Niko Maas, Dieter Schramm:
Hybrid State Estimation-A Contribution Towards Reliability Enhancement of Artificial Neural Network Estimators. IEEE Trans. Intell. Transp. Syst. 23(7): 6337-6346 (2022) - [c6]Maximilian Jarofka, Philipp Maximilian Sieberg, Christian Hürten, Tim Benedens, Ricarda Peters, Frédéric Etienne Kracht, Dieter Schramm:
Template for Preparation of Papers for IEEE Sponsored Conferences & Symposia. ITSC 2022: 1548-1553 - [c5]Christian Hürten, Philipp Maximilian Sieberg, Dieter Schramm:
Generating a Multi-fidelity Simulation Model Estimating the Models' Applicability with Machine Learning Algorithms. SIMULTECH 2022: 131-141 - 2020
- [c4]Philipp Maximilian Sieberg, Christian Hürten, Dieter Schramm:
Representation of an Integrated Non-Linear Model-Based Predictive Vehicle Dynamics Control System by a Co-Active Neuro-Fuzzy Inference System. IV 2020: 572-577
2010 – 2019
- 2019
- [c3]Philipp Maximilian Sieberg, Sebastian Blume, Nele Harnack, Niko Maas, Dieter Schramm:
Hybrid State Estimation Combining Artificial Neural Network and Physical Model. ITSC 2019: 894-899 - [c2]Sebastian Blume, Philipp Maximilian Sieberg, Niko Maas, Dieter Schramm:
Neural Roll Angle Estimation in a Model Predictive Control System. ITSC 2019: 1625-1630 - [c1]Matthias Bahr, Sebastian Reicherts, Philipp Maximilian Sieberg, Luca Morss, Dieter Schramm:
Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning. SIMULTECH 2019: 36-46
Coauthor Index
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