Artificial Intelligence and Data Integration

Research Area

ICD and its Chair for Computing in Architecture investigate applications of artificial intelligence (AI) for architecture, engineering, and construction, often in collaboration with researchers at the Max Planck Institute for Intelligent Systems and in Cybervalley. In optimization, we investigate and improve the performance of (multi-objective) optimization algorithms. In machine learning (ML), we investigate the use of ML in interactive design tools and visualizations, for example to predict the behavior of material systems. In symbolic AI, we investigate the integration of Knowledge Graphs and AI Planning into multi-disciplinary co-design workflows.

Selected Publications

  1. 2022

    1. Wortmann, T., Cichocka, J., & Waibel, C. (2022). Simulation-based Optimization in Architecture and Building Engineering—Results from an International User Survey in Practice and Research. Energy and Buildings, 259, 111863. https://doi.org/10.1016/j.enbuild.2022.111863
  2. 2021

    1. Natanian, J., & Wortmann, T. (2021). Simplified evaluation metrics for generative energy-driven urban design: A morphological study of residential blocks in Tel Aviv. Energy and Buildings, 240, 110916. https://linkinghub.elsevier.com/retrieve/pii/S0378778821002000
    2. Petrov, M., & Wortmann, T. (2021, April). Latent fitness landscapes - Exploring performance within the latent space of post-optimization results. Proceedings of the Symposium on Simulation for Architecture & Urban Design.
    3. Wortmann, T., & Natanian, J. (2021). Optimizing solar access and density in Tel Aviv: Benchmarking multi-objective optimization algorithms. Journal of Physics: Conference Series, 2042(1), 012066. https://doi.org/10.1088/1742-6596/2042/1/012066
    4. Natanian, J., Luca, F. D., Wortmann, T., & Capeluto, G. (2021). The Solar Block Generator: an additive parametric method for solar driven urban block design. Journal of Physics: Conference Series, 2042(1), 012049. https://doi.org/10.1088/1742-6596/2042/1/012049
    5. Waibel, C., Zhang, R., & Wortmann, T. (2021, April). Physics Meets Machine Learning: Coupling FFD with Regression Models for Wind Pressure Prediction on High-Rise Facades. Proceedings of the Symposium on Simulation for Architecture & Urban Design.

Contact Information

This image shows Thomas Wortmann

Thomas Wortmann

Ph.D.,Dipl.-Ing., M.Sc.

Tenure Track Professor | Architectural Computing

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