Technische Universität Berlin
Philipp Geyer is establishing the group of Digital and Sustainable Architecture as guest professor at TU Berlin. He leads research in the field of digital modelling, simulation and intelligent computer methods at TU Berlin and KU Leuven. In this field, he developed innovative methods of machine learning for performance-based and sustainable design embedded in digital modelling as PI in the DFG researcher unit FOR 2363. He coordinated the project H-DisNet on thermo-chemical energy networks, which was funded by the EU Horizon 2020 program. He is vice chair of the European Group of Intelligent Computing in Engineering (eg-ice.org). As associated editor and reviewer, he works for internationally top-ranking journals as Advanced Engineering Informatics, Applied Energy, Proceedings of the IEEE and Automation in Construction and has published his research in more than 80 papers.
1. Singh, M. M., Geyer, P. (2020): Information requirements for multi-level-of-development BIM using sensitivity analysis for energy performance. Advanced Engineering Informatics, 43, 101026, https://doi.org/https://doi.org/10.1016/j.aei.2019.101026
2. Singaravel, S., Suykens, J., Geyer, P. (2019): Deep convolutional learning for general early design stage prediction models. Advanced Engineering Informatics, 42, 100982, https://doi.org/https://doi.org/10.1016/j.aei.2019.100982
3. Geyer P., Singaravel S. (2018). Component-based machine learning for performance prediction in building design. Applied Energy, 228, 1439-1453. doi: 10.1016/j.apenergy.2018.07.011.
4. Geyer P., Buchholz M., Buchholz R., Provost M. (2017). Hybrid thermo-chemical district networks - Principles and technology. Applied Energy, 186, 480-491. doi: 10.1016/j.apenergy.2016.06.152.
5. Schlueter A., Geyer P., Cisar S. (2016). Analysis of Georeferenced Building Data for the Identification and Evaluation of Thermal Microgrids. Proceedings of the IEEE, 104 (4), 713-725. doi: 10.1109/JPROC.2016.2526118.
6. Geyer P, Schlüter A (2016): Clustering and Fuzzy Reasoning as Data Mining Methods for the Development of Retrofit Strategies for Building Stocks. In: Song H, Srinivasan R, Sookoor T, Jeschke S (Hg.): Smart Cities - Foundations, Principles, and Applications, Wiley, 437–472.
7. Geyer P., Schlueter A. (2014). Automated metamodel generation for Design Space Exploration and decision-making - A novel method supporting performance-oriented building design and retrofitting. Applied Energy, 119, 537-556. doi: 10.1016/j.apenergy.2013.12.064.
8. Shao Y., Geyer P., Lang W. (2014). Integrating requirement analysis and multi-objective optimization for office building energy retrofit strategies. Energy and Buildings, 82, 356-368. doi: 10.1016/j.enbuild.2014.07.030.
9. Geyer P. (2012). Systems modelling for sustainable building design. Advanced Engineering Informatics, 26 (4), 656-668. doi: 10.1016/j.aei.2012.04.005.
10. Geyer P. (2009). Component-oriented decomposition for multidisciplinary design optimization in building design. Advanced Engineering Informatics, 23 (1), 12-31. doi: 10.1016/j.aei.2008.06.008.