Artificial Intelligence

Artificial Intelligence

Deep Learning prediction of glass breakage
Deep Learning prediction of glass breakage

We research the application as well as new and further development of artificial intelligence (AI) algorithms in a variety of ways in glass and façade construction in order to develop innovative and efficient solutions for a more sustainable yet safe future. Our research projects range from data-supported material modeling of glass, interlayer and laminated glass to the generative design of glass components and façades as well as AI for the control and regulation of cyber-physical systems in the façade.

By using domain-informed machine learning and deep learning on various scales, we can predict and optimize material properties as well as system behavior more precisely. This starts with the application of deep learning models for the detection and prediction of fracture patterns through to the constitutive modeling of polymer materials and the calibration of physics-informed prediction models of glass structures. of the deep learning of glazing. In the future, this research will enable precise and reliable prediction of high-performance and durable glass products with higher economic efficiency.

In our research on generative design, we analyse generative AI algorithms with regard to their suitability for designing innovative and aesthetically pleasing glass components and façades. These are optimised taking into account various criteria such as statics, sustainability and acoustics. In addition, we are working on the use of AI algorithms to control and regulate cyber-physical façade systems that interact with users with the aim of co-optimising user requirements as well as building physics, statics and aesthetics.