| Improving production efficiency with a digital twin based on anomaly detection J Trauer, S Pfingstl, M Finsterer, M Zimmermann Sustainability 13 (18), 10155, 2021 | 30 | 2021 |
| On integrating prior knowledge into Gaussian processes for prognostic health monitoring S Pfingstl, M Zimmermann Mechanical Systems and Signal Processing 171, 108917, 2022 | 21 | 2022 |
| On the potential of extending aircraft service time using load monitoring S Pfingstl, D Steinweg, M Zimmermann, M Hornung Journal of Aircraft 59 (2), 377-385, 2022 | 9 | 2022 |
| Crack detection zones: Computation and validation S Pfingstl, M Steiner, O Tusch, M Zimmermann Sensors 20 (9), 2568, 2020 | 9 | 2020 |
| Warped Gaussian processes for predicting the degradation of aerospace structures S Pfingstl, C Braun, A Nasrollahi, FK Chang, M Zimmermann Structural Health Monitoring 22 (4), 2531-2546, 2023 | 6 | 2023 |
| Reinforcement learning for structural health monitoring based on inspection data S Pfingstl, YN Schoebel, M Zimmermann 8th Asia-Pacific Workshop on Structural Health Monitoring, 203-210, 2021 | 5 | 2021 |
| Strain-based structural health monitoring: computing regions for critical crack detection S Pfingstl, M Zimmermann Structural Health Monitoring 2019, Stanford University, 132-139, 2019 | 5 | 2019 |
| Estimation of composite laminate ply angles using an inverse Bayesian approach based on surrogate models M Franz, S Pfingstl, M Zimmermann, S Wartzack Proceedings of the Design Society 2, 1569-1578, 2022 | 2 | 2022 |
| Warped Gaussian processes for prognostic health monitoring S Pfingstl, C Braun, M Zimmermann STRUCTURAL HEALTH MONITORING 2021, 2021 | 2 | 2021 |
| Comparison of error measures and machine learning methods for strain-based structural health monitoring S Pfingstl, O Tusch, M Zimmermann STRUCTURAL HEALTH MONITORING 2021, 2021 | 2 | 2021 |
| Predicting Crack Growth and Fatigue Life with Surrogate Models S Pfingstl, JI Rios, H Baier, M Zimmermann arXiv preprint arXiv:2008.02324, 2020 | 2 | 2020 |
| On the potential of extending aircraft service time using a fatigue damage index S Pfingstl, D Steinweg, M Zimmermann, M Hornung arXiv preprint arXiv:2008.03138, 2020 | 1 | 2020 |
| Gaussian Processes for Prognostics SB Pfingstl Technische Universität München, 2023 | | 2023 |
Markus ZimmmermannTechnical University of Munich (TUM)在 tum.de 的电子邮件经过验证
