--- title: "Uppsala University Assistant Professor: Generative AI as Engineering Design Engine and Cross-Disciplinary Bridge" author: "Unique Research" sourcePublication: "Unique Research (FFCAP English library)" originalPublishedAt: "2023-11-07T16:30:00+08:00" canonical: "https://ffcap.cn/en/research/20231107a0793800" source: "https://ffcap.cn/en/research/20231107a0793800" language: "en" cnSourceUrl: "https://view.inews.qq.com/a/20231107A0793800" --- # Uppsala University Assistant Professor: Generative AI as Engineering Design Engine and Cross-Disciplinary Bridge _Unique Research · Insight · November 7, 2023 · Adapted from Didem Gürdür Broo, IEEE Spectrum · Original: spectrum.ieee.org/ai-design_ **Author:** **Didem Gürdür Broo** — Assistant Professor, Department of Information Technology, Uppsala University (Sweden). Leads the cyber-physical systems lab on data-centric methods for sustainable, human-centered intelligent systems (collaborative robots, AVs, smart cities). Previously Marie Skłodowska-Curie Fellow at Stanford; researcher at Cambridge. ## How genAI is guiding innovative design 2023 may be remembered as genAI’s year one. Whether it makes life better remains open — but new AI tools keep shipping, and engineers gain by experimenting and folding them into design. Examples already exist. For Aston Martin’s **DBR22** concept, designers used AI integrated into Divergent Technologies’ digital 3D software to optimize rear subframe shape and layout — organic, skeletal forms via AI form exploration, then additive manufacturing. Aston Martin says weight fell while stiffness held; same design/manufacture process planned for upcoming low-volume models. NASA research engineer **Ryan McClelland** calls commercial-AI-designed 3D-printed parts “evolved structures” — unfamiliar-looking yet higher structural load at lower weight than traditional components, in a fraction of the design time. AI iterates design mutations toward high performance. In such environments, co-design with genAI, high-quality structured data, and well-studied parameters clearly yields more creative and effective designs. ## How genAI sparks engineering design In January last year the author began experimenting with genAI for cyber-physical systems research (smart homes, AVs) — physical + compute components with feedback loops. Designing them needs creative collaboration between designers and engineers — slow. Could AI generators widen option space, speed iteration, or bridge disciplines? She sought inspiration more than prescriptions. Text and music generators were fun; image generators fit best. Platforms tried: Craiyon, DALL·E 2, Midjourney, NightCafé, Stable Diffusion — best combo **Midjourney + Stable Diffusion** (proprietary online vs. free local open-source). First experiment: co-design a self-sustaining jellyfish robot planned for Uppsala’s lab — nature-inspired CPS collecting microplastics as part of marine ecosystems. Usual lab loop: brainstorm → sketch → CAD → simulate → prototype → test — collaborative but experience-heavy. Vague early prompts failed; more precise prompts synced with vision; textures/materials iterated until several designs satisfied. Minutes for first results; hours to change, restate, combine successful elements. Good prompts are specific (purpose) yet leave room for surprise — balance known wants vs. unknown-but-maybe-wanted. Unspecified attributes may be randomly assigned; include composition instructions for product design. ## Controlling genAI outcomes is nearly impossible Trying to control co-creation sometimes worked, mostly failed. Humanoid-robot “technical design” prompts often injected meaningless pseudo-letters — visual quirks from blueprint-heavy training data, not real language. Jellyfish → octopus failed oddly (engineers see them as similar) — likely training-set gaps; later octopus images slightly less crude still anatomically wrong. Promising jellyfish designs reviewed with the team for aesthetic/functional cues transferable to physical prototypes (arched umbrella tops → protective shells; flowing tentacles → flexible manipulators). None copied directly; organic shapes sparked useful ideas and research — resisting premature lock-in. IDEO CEO Tim Brown: prototypes “slow us down so we can speed up,” avoiding costly early over-complexity and clinging to weak ideas. ## Even unsuccessful genAI results can inspire Image generators for smart-city communication complexity gave creative freedom vs. Visio/Illustrator — but poorly depicted flows and interactions; models don’t grasp context or relational meaning. After months pushing generators to limits: useful for exploration, inspiration, and quick brainstorm illustrations. Even unrealistic images push new directions; even bad flow diagrams spark productive brainstorms. Co-creation needs persistence. Fast good results delight; specific agendas make tools hard to steer. Human control of iterations is weak; outcomes unpredictable. The author’s own thinking, expression, and taste shifted mid-process — sometimes abandoning initial specs for AI-suggested visuals; sometimes loosening constraints so the AI had more freedom. Process changes both design and designer. Generators have updated toward more predictability — negative if you want unconventional concepts; understandable if you need control. Future: tools that execute predictably under clear constraints; image generators integrated into engineering tools and trained on their data. Serious ethics: amplifying demographic/other biases; misinformation; privacy and IP; livelihoods of artists/writers. Transparency, oversight, accountability on data sources, generation, and downstream use are required — users must treat these issues seriously. If used ethically, tools help engineers explore future systems, leave comfort zones, and create productive “chaos” before rigorous engineering imposes order — thinking differently, seeing connections, considering future impacts, designing innovative sustainable solutions. _7 CN images; attach on ingest._ — END — --- Editorial English edition published on UniqueCapital: https://ffcap.cn/en/research/20231107a0793800 On-site reading page: https://ffcap.cn/en/research/20231107a0793800