SPP 2353:  Daring More Intelligence - Design Assistants in Mechanics and Dynamics

• Subject Area: Mechanical and Industrial Engineering ; Construction Engineering and Architecture ; Geosciences ; Computer Science, Systems and Electrical Engineering ; Mathematics ; Thermal Engineering/Process Engineering
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 460725022

To respect ecological and societal responsibilities and challenges as well as to account for stricter and more complex regulations, future systems design has to become increasingly multidisciplinary. Computer-based support as employed today in mechanics and dynamics, mostly limited to system analysis only, is not sufficient anymore. Even in advanced design workflows, usually large-scale, simulation-driven parameter studies are conducted and inspected only manually to iteratively alter a candidate design based on experience and expert knowledge. This process is not only very time consuming but also typically based on subjective rather than on formalised mathematical objectives.The research in the established Priority Programme shall aim at the development of design assistance systems combining methods from optimisation, artificial intelligence, and dynamics/mechanics to assist in and partially automate the interdisciplinary design of engineering systems. This may not only result in designs that are actually optimal with respect to formalized criteria, but such design assistants may equip design engineers with an artificial intuition supplementing their own specialized expertise. This way, criteria nowadays only considered in later design stages may be taken into account early on, improving resulting systems in a much more fundamental manner than today’s incremental improvements following established design paradigms.The key to realizing design assistant systems of practical impact in dynamics and mechanics is to go beyond the state of the art in system analysis, optimisation, and design by integrating methods from artificial intelligence and machine learning. For instance, machine learning methods can be valuable tools to infer surrogate models and response surfaces that can be used to make manageable the calculation effort for large-scale analysis as part of automated design procedures relying on multicriteria optimisation. Methods from artificial intelligence may even directly make certain creative design decisions. However, since machine learning and artificial intelligence have recently thrived mostly in fields far from the design of dynamic systems, it is, as yet, rather unclear which methods will be best-suited and, in particular, how they can be combined with system analysis and optimisation to achieve better designs. Ideally, the design assistant components should be highly flexible with easily accessible interfaces so that they can be combined modularly to build up increasingly holistic, assisted design procedures, and to serve as a foundation for continued research in the second funding period.

• DFG Programme: Priority Programmes

Projects

• AI based design assistance system for soft robotics - Optimizing complex systems based on the smallest design entity (Applicant de Payrebrune, Kristin )
• Daring More Intelligence – Design Assistants in Mechanics and Dynamics (SPP 2353) - Coordination Proposal (Applicant Eberhard, Peter )
• Data- and Response-Surface-Driven Design Assistant for Controlled Flexible Multibody Systems (Applicants Bestle, Dieter ;  Eberhard, Peter )
• Data-driven machine learning enhanced optimisation of vehicle crashworthiness design (Applicant Stoffel, Marcus )
• Design to Acoustics through Deep Learning (Applicants Langer, Sabine C. ;  Lüddecke, Timo )
• Designing complex behaviour: novel pathways for assisting design based on dynamics-informed machine learning in structural mechanics (Applicant Stender, Merten )
• Hybrid Modeling for Data-enhanced Multiobjective Optimization of Multibody Systems (Applicants Peitz, Sebastian ;  Sextro, Walter )
• Intelligent design assistance for personalized medical surgery based on concentric tube continuum robots (Applicants Flaßkamp, Kathrin ;  Sattel, Thomas )
• Modes, Motions, and Morphology - Design Optimization for Legged Robots and Other Periodically Operating Mechanisms (Applicant Remy, C. David )
• Shape optimizations using AI agents to accelerate numerical flow field simulation and to control the island model for massive parallelization (Applicant Riedelbauch, Stefan )

• Spokesperson: Professor Dr.-Ing. Peter Eberhard