Using Technology To Enhance Creativity In Engineering

While people are busy creating their cartoon characters and having fun with the new OpenAI GPT-4o image generation tool, I decided to test it on something a bit different: engineering use cases. Can a creative image generation model support civil and infrastructure engineering? It turns out, yes, with the right guidance (although not quite there yet). I explored three practical applications: Sea Level Rise (SLR) Simulation Scenarios Climate adaptation planning often relies on GIS maps and simulations. GPT-4o can create illustrative views of how a coastline or neighborhood might change under different sea level rise scenarios. These visuals are not analytical models, but they’re helpful for community engagement, early design workshops, and raising awareness about climate impacts. Construction Staging and Phasing Visualizing site conditions across phases, before excavation, during substructure work, and at completion helps teams, clients, and the public understand project timelines. GPT-4o can quickly generate visual representations based on a short prompt for different stages. This can accelerate site planning, communication, and permitting workflows. Urban Revitalization and Streetscape Improvements Instead of relying on generic renderings, GPT-4o can instantly generate visuals for urban renewal concepts, such as adding green spaces, bike lanes, or pedestrian-friendly designs. It can complement site sketches or planning documents, helping planners and engineers quickly prototype ideas visually. Let’s be clear: AI doesn’t replace engineering expertise. These tools don’t understand structural design, drainage, or traffic volumes. However, early-stage communication, idea generation, and stakeholder alignment can significantly boost human engineers productivity and creativity. We are not being replaced, we are being augmented. #AI #GPT4o #CivilEngineering #UrbanDesign #ClimateAdaptation #ConstructionTech #AIDesignTools #OpenAI

What if the biggest value in AI doesn’t come from efficiency, but creativity?   Take R&D. It’s an inherently creative endeavor, but it's also expensive and time-consuming—over time, each dollar buys less and less innovation. While the drive for efficiency gains from LLMs like ChatGPT take up most of the limelight, our new report finds more specialized foundation models could double the pace of R&D… and unlock a half trillion annually: https://lnkd.in/gUPj6S_K Three ways AI could help unlock that value: -Generating more design ideas faster: From creating novel proteins to remodeling retail locations, engineering more efficient electronics, and more, industry- and task-specific models can surface unexpected ideas -Accelerating evaluation through digital twins: Before putting one car in one wind tunnel, put thousands of virtual designs through an AI-powered model—increasing speed and possibility -Unlimited research assistants: Here’s where LLMs come in—use gen AI tools and agents for everything from synthesizing customer feedback, social media, and other data to researching, automating documentation, and more That’s my 30,000-foot view—dive into the paper to get more real-life examples, breakouts of economic potential by industry, and more. And big thanks to the team behind this and to Alex Singla, Alexander Sukharevsky, Lareina Yee, Elia Berteletti, and Michael Chui for leading here.

The future of engineering is generative, intelligent, and deeply domain-aware. At #Siemens, we're building a new kind of Foundation Model—not just trained on internet-scale data, but grounded in the physics, geometry, and logic of the industrial world. While models like GPT-4 have reshaped content creation and conversation, our Foundation Model aims to transform how we design, simulate, and automate everything from jet engines to energy grids. Trained on rich engineering data—from CAD, CAE, DM and automation logic—this model doesn't just predict words. It understands parts, tolerances, constraints, workflows, and real-world behavior. This isn’t about replacing engineers. It’s about augmenting human creativity with AI that speaks the language of design, manufacturing, and systems. Integrated into NX, Teamcenter, Industrial Copilot, and Digital Manufacturing platforms, our Foundation Model will empower engineers to: - Generate complex geometry from intent - Predict performance without full simulation - Translate ideas into production-ready models—in minutes This is what domain-specific AI at industrial scale looks like. https://lnkd.in/gq47QH7S #IndustrialAI #SiemensXcelerator #IndustrialFoundationModel #GenerativeEngineering #AIinDesign