| Estimating electric motor temperatures with deep residual machine learning W Kirchgässner, O Wallscheid, J Böcker IEEE Transactions on Power Electronics 36 (7), 7480-7488, 2020 | 102 | 2020 |
| Data-driven permanent magnet temperature estimation in synchronous motors with supervised machine learning: A benchmark W Kirchgässner, O Wallscheid, J Böcker IEEE Transactions on Energy Conversion 36 (3), 2059-2067, 2021 | 66 | 2021 |
| Deep residual convolutional and recurrent neural networks for temperature estimation in permanent magnet synchronous motors W Kirchgässner, O Wallscheid, J Böcker 2019 IEEE International Electric Machines & Drives Conference (IEMDC), 1439-1446, 2019 | 61 | 2019 |
| Controller design for electrical drives by deep reinforcement learning: A proof of concept M Schenke, W Kirchgässner, O Wallscheid IEEE Transactions on Industrial Informatics 16 (7), 4650-4658, 2019 | 57 | 2019 |
| Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors O Wallscheid, W Kirchgässner, J Böcker 2017 International joint conference on neural networks (IJCNN), 1940-1947, 2017 | 53 | 2017 |
| Toward a reinforcement learning environment toolbox for intelligent electric motor control A Traue, G Book, W Kirchgässner, O Wallscheid IEEE Transactions on neural networks and learning systems 33 (3), 919-928, 2020 | 49 | 2020 |
| Empirical evaluation of exponentially weighted moving averages for simple linear thermal modeling of permanent magnet synchronous machines W Kirchgässner, O Wallscheid, J Böcker 2019 IEEE 28th International Symposium on industrial electronics (ISIE), 318-323, 2019 | 38 | 2019 |
| Transferring online reinforcement learning for electric motor control from simulation to real-world experiments G Book, A Traue, P Balakrishna, A Brosch, M Schenke, S Hanke, ... IEEE Open Journal of Power Electronics 2, 187-201, 2021 | 32 | 2021 |
| Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning W Kirchgässner, O Wallscheid, J Böcker Engineering Applications of Artificial Intelligence 117, 105537, 2023 | 25 | 2023 |
| Gym-electric-motor (GEM): A python toolbox for the simulation of electric drive systems P Balakrishna, G Book, W Kirchgässner, M Schenke, A Traue, ... Journal of Open Source Software 6 (58), 2498, 2021 | 18 | 2021 |
| Learning thermal properties and temperature models of electric motors with neural ordinary differential equations W Kirchgässner, O Wallscheid, J Böcker 2022 International Power Electronics Conference (IPEC-Himeji 2022-ECCE Asia …, 2022 | 5 | 2022 |
| Reinforcement learning course material W Kirchgässner, M Schenke, O Wallscheid, D Weber Paderborn Univ., Paderborn, Germany, 2020 | 4 | 2020 |
| HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores N Förster, W Kirchgässner, T Piepenbrock, O Schweins, O Wallscheid arXiv preprint arXiv:2401.11488, 2024 | | 2024 |
| Application of Thermal Neural Networks on a Small-Scale Electric Motor W Kirchgaessner, D Woeckinger, O Wallscheid, G Bramerdorfer, ... IKMT 2022; 13. GMM/ETG-Symposium, 1-6, 2022 | | 2022 |
Oliver WallscheidScientist, Siegen UniversityBestätigte E-Mail-Adresse bei uni-siegen.de
