Dr. Christian Jaeger
Dr. Christian Jaeger
ZHAW
School of Engineering
Machine Learning in Optimal Control for Industry
Technikumstrasse 71
8400 Winterthur
Projects
- Automated Commissioning of Heat Pumps / Project leader / completed
- Feasibility Study Reinforcement Learning for Heating Systems / Deputy project leader / completed
- Adaptive energy management system for buildings / Project leader / completed
- Advanced Electronic Personal Dosemeter / Team member / completed
- Model of a Sieving machine (continuation) / Team member / completed
Publications
Articles in scientific journal, peer-reviewed
- Wüthrich, M., Gubser, M., Elspass, W. J., & Jaeger, C. (2021). A novel slicing strategy to print overhangs without support material. Applied Sciences, 11(18), 8760. https://doi.org/10.3390/app11188760
- Füchslin, R. M., Dumont, E., Flumini, D., Fuchs, H. U., Hauser, H., Jaeger, C., Scheidegger, S., Schönenberger-Deuel, J., Lichtensteiger, L., Luchsinger, R. H., & Weyland, M. (2014). Morphological control : a design principle for applications in space science. Journal of the British Interplanetary Society, 67, 305–313. http://adsabs.harvard.edu/abs/2014JBIS...67..305F
Written conference contributions, peer-reviewed
- Bolt, P., Ziebart, V., Jaeger, C., Schmid, N., Stadelmann, T., & Füchslin, R. M. (2024). A simulation study on energy optimization in building control with reinforcement learning [Conference paper]. In C. Y. Suen, A. Krzyzak, M. Ravanelli, E. Trentin, C. Subakan, & N. Nobile (Eds.), Artificial Neural Networks in Pattern Recognition. Springer. https://doi.org/10.1007/978-3-031-71602-7_27
- Bolt, P., Ziebart, V., Jaeger, C., Ritzmann, R., Meier, O., & Füchslin, R. M. (2018). Model predictive control for building automation [Conference paper]. EuroSun 2018 : Conference Proceedings, 1330–1341. https://doi.org/10.18086/eurosun2018.11.05
- Jaeger, C., Housseini, R., Hertwig, M., Hofer, T., & Füchslin, R. M. (2014). Performance prediction and optimization for industrial sieves by simulation : a two-tier approach [Conference paper]. In C. Deatcu & J. Wittman (Eds.), ASIM 2014: 22. Symposium Simulationstechnik: 3. bis 5. September 2014: HTW Berlin: 2, Tagungsband: Teil II (pp. 299–306). Argesim. https://doi.org/10.21256/zhaw-1653
- Bernhardsgrütter, R., Senn, C. W., Füchslin, R. M., Jaeger, C., Nakajima, K., & Hauser, H. (2014). Employing L-systems to generate mass-spring networks for morphological computing [Conference paper]. Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), 168–171. https://doi.org/10.21256/zhaw-1660