W. Kirchgässner, O. Wallscheid, and J. Böcker, “Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning,”10.1016/j.engappai.2022.105537__Engineering Applications of Artificial Intelligence, vol. 117, Art. no. 105537, 2022, doi:10.1016/j.engappai.2022.105537 .

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations,”10.23919/ipec-himeji2022-ecce53331.2022.9807209__2022, doi:10.23919/ipec-himeji2022-ecce53331.2022.9807209 .

W. Kirchgässner, D. Wöckinger, O. Wallscheid, G. Bramerdorfer, and J. Böcker, “Application of Thermal Neural Networks on a Small-Scale Electric Motor,” in IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1–6.

G. Book et al., “Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments,”10.1109/ojpel.2021.3065877__IEEE Open Journal of Power Electronics, pp. 187–201, 2021, doi:10.1109/ojpel.2021.3065877 .

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark,”10.1109/tec.2021.3052546__IEEE Transactions on Energy Conversion, vol. 36, no. 3, pp. 2059–2067, 2021, doi:10.1109/tec.2021.3052546 .

P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, and O. Wallscheid, “gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems,”10.21105/joss.02498__Journal of Open Source Software, Art. no. 2498, 2021, doi:10.21105/joss.02498 .

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning,” arXiv preprint arXiv:2103.16323. 2021.

A. Traue, G. Book, W. Kirchgässner, and O. Wallscheid, “Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control,”10.1109/tnnls.2020.3029573__IEEE Transactions on Neural Networks and Learning Systems, pp. 1–10, 2020, doi:10.1109/tnnls.2020.3029573 .

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Estimating Electric Motor Temperatures with Deep Residual Machine Learning,”10.1109/tpel.2020.3045596__IEEE Transactions on Power Electronics, vol. 36, no. 7, pp. 7480–7488, 2020, doi:10.1109/tpel.2020.3045596 .

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning,” arXiv preprint arXiv:2001.06246, 2020.

M. Schenke, W. Kirchgässner, and O. Wallscheid, “Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept,”10.1109/tii.2019.2948387__IEEE Transactions on Industrial Informatics, pp. 4650–4658, 2019, doi:10.1109/tii.2019.2948387 .

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines,”10.1109/isie.2019.8781195__2019, doi:10.1109/isie.2019.8781195 .

W. Kirchgässner, O. Wallscheid, and J. Böcker, “Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors,”10.1109/iemdc.2019.8785109__2019, doi:10.1109/iemdc.2019.8785109 .

O. Wallscheid, W. Kirchgässner, and J. Böcker, “Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors,”10.1109/ijcnn.2017.7966088__2017, doi:10.1109/ijcnn.2017.7966088 .