Generative engineering
AI generative design transforms design engineering by enabling the rapid generation, visualization, and comparison of valid, constraint-driven design variants.
7 min reading
AI Generative design : How can you generate design with AI?
AI generative design transforms design engineering by enabling the rapid generation, visualization, and comparison of valid, constraint-driven design variants.
Product development today is more complex than ever. Design engineers face increasingly compressed timelines, growing systems complexity, and rising demands for cost efficiency and sustainability. Against this backdrop, AI generative design is emerging as a transformative capability—reshaping how we create, validate, and optimize designs across industries.
By integrating artificial intelligence into the earliest stages of engineering, design teams are no longer bound by linear, manual modeling processes. Instead, they can use AI to generate multiple design alternatives in a fraction of the time—each one tailored to meet predefined technical and functional constraints. This shift marks a new era: design generation becomes intelligent, scalable, and insight-driven.
What is AI-generative design ?
AI-generative design in engineering refers to using algorithms to help engineers explore different ways a product or system could be designed—within a defined set of constraints. These constraints might include:
- Geometry rules
- Functional architectures
- Component dependencies
- Design standards
- Manufacturing requirements
While traditional CAD tools require engineers to model each variant manually, AI-powered generative design helps augment this process. Engineers set their intent—such as spatial constraints, interface rules, or configuration requirements and the AI generates multiple valid design options that already respect engineering logic.
In engineering environments, this means engineers no longer need to model every variant manually. Instead, they act as orchestrators, setting high-level goals and validating AI-generated outputs that already meet key engineering rules and product constraints.
Key technologies behind AI generative design
AI generative design is not a single algorithm—it’s a convergence of several powerful technologies that enable the generation of valid, optimized, and domain-compliant design variants. Together, these technologies make it possible to move from manual drafting to intelligent design exploration.
- Constraint based reasoning
At the core of AI-driven design is constraint based reasoning. Engineers define the rules a design must follow, such as spacial limitations, safety clearances, or functional relationships, and the system uses these constraints to generate only feasible solutions. This ensures that every proposed variant respects core engineering requirements from the very start.
- Domain specific rule libraries
Unlike generic optimization tools, engineering design AI systems rely on domain-specific rule libraries—collections of verified engineering logic tailored to industries like automotive, aerospace, or industrial machinery. These rules encode tribal knowledge, standards, and design heuristics, allowing the AI to act with relevance and precision.
For example, Dessia’s AI libraries are built on curated rule sets that reflect actual engineering constraints, such as connector spacing in EV harness design or curvature radius in hydraulic routing. This ensures the generated output is not just creative, but also technically compliant.
- Automated layout engines
Automated layout engines process spatial and functional inputs to generate component placements, system paths, or full architecture sketches. Whether it’s routing a cable through a constrained volume or positioning components within a battery pack, these engines use algorithms to explore multiple configuration possibilities, far faster than any manual method.
The result: engineers receive multiple layout proposals that already satisfy routing logic, collision constraints, and performance boundaries.
- Geometric and topological variant generation
AI systems generate not just one model, but entire families of geometries and topologies, allowing teams to compare and select the most effective configuration. These variants differ in shape, structure, or layout logic, but all adhere to the same set of rules, offering engineers the flexibility to evaluate trade-offs between space, cost, weight, and performance.
This accelerates concept selection and enables earlier validation in the product development cycle.
How does AI generative design work with Dessia?
In practice, AI generative design follows a structured, rule-driven process that augments engineering expertise. The objective is not to create artistic variations but to automatically produce valid engineering design alternatives based on predefined logic.
1. Define the use case and upload inputs
The process begins when engineers choose a specific use case—such as harness routing, battery architecture, or functional partitioning—and upload the required data to the platform. Inputs can include 3D geometry files, Excel spreadsheets, or other structured information relevant to the design space.
2. Integrate your knowledge and know-how
Once inputs are loaded, Dessia combines symbolic AI and statistical AI to generate optimized design variants.
The symbolic layer draws on internally developed rule libraries that encode domain-specific logic such as spatial constraints, connectivity conditions, component spacing, and compliance requirements—ensuring that all generated variants meet engineering standards.
At the same time, statistical AI models trained on historical design data introduce a predictive layer to the process. Neural networks, including convolutional and graph-based architectures, help identify patterns, guide design decisions, and improve overall quality and speed.
3. Generate valid design variants
Using these combined constraints, Dessia’s AI libraries automatically generate a wide range of valid design configurations. Each variant respects the encoded logic, allowing engineers to explore feasible options without manual iteration.
4. Filter, visualize, and compare
Design engineers interact with the results through Dessia’s built-in visualization tools. Instead of reviewing solutions one by one, they can apply filters, use dynamic plots, and compare options based on key parameters.
5. Export and integrate
The Dessia AI-powered platform integrates seamlessly with your existing environment. Connect directly with your CAD & PLM tools and deploy the platform on the cloud or on-premises—ensuring scalability and data security.
Conclusion
AI is reshaping the role of design engineers—turning manual modeling into intelligent, rule-driven exploration. By integrating AI throughout the design process, platforms like Dessia enable engineers to generate first time right design variants at scale, without sacrificing control or technical rigor. This isn’t about replacing engineering expertise—it’s about amplifying it. In a world of growing complexity, AI-powered generative design is becoming essential to deliver better solutions, faster.
Published on
26.05.2025
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