Sub-area within the DFG Research Training Group GRK 3068 ("Climate-informed engineering")

Motivation and goals of the research training group

The impacts of climate change on civil infrastructure, ecosystems, and society require urgent action. While advancements in climate models enable precise predictions of future climate scenarios, integrating the climate models into engineering practices remains insufficient. Addressing this critical gap, the DFG Research Training Group fosters interdisciplinary collaboration between climate science and engineering disciplines. The primary objective is to establish robust frameworks and methodologies for developing climate-resilient and sustainable solutions, promoting the coalescence of interdisciplinary cutting-edge climate science with engineering innovations. The interdisciplinary approach facilitates climate change mitigation and adaptation, in alignment with global sustainability objectives, particularly those outlined in the United Nations Sustainable Development Goals. Furthermore, the DFG Research Training Group offers a comprehensive educational and research ecosystem, including collaborative projects, access to advanced analytical tools, and dedicated mentorship. Next-generation researchers will be prepared to design resilient infrastructure systems, to enhance resource efficiency, and to develop adaptive engineering processes that address the complex challenges posed by climate change.

Research objectives of sub-area A1

Sub-area A1, entitled "Coupling climate models and indoor climate models for climate-informed engineering", pioneers the seamless integration of global, regional, and local climate data with advanced high-resolution indoor climate models to address the challenges of climate change and extreme weather events from an civil engineering perspective. By employing a formal "climate-informed engineering" (CIE) metamodel grounded in applied category theory and attributed graph grammar, sub-area A1 establishes a robust and scalable foundation for coupling climate data with engineering semantics. Key innovations include algorithmic building information modeling (aBIM), which enhances semantic modeling for integration, scalability, and adaptability, alongside AI-driven algorithms and IoT-enabled sensing and actuation systems for dynamic climate adaptation of buildings. Central to its objectives is the development of resilient, sustainable, and adaptive engineering strategies, exemplified by the "TUHH Digital Campus Twin", a comprehensive digital twin reference architecture that, in close collaboration with the other sub-areas, will serve as a validation and testing platform for adaptive, climate-informed smart building solutions. By advancing interdisciplinary, data-driven methodologies, sub-area A1 contributes to global sustainability objectives, fostering occupant comfort, energy efficiency, and carbon footprint reduction, while shaping the future of climate-informed engineering practices.