Agentic AI for sustainable engineering design

About the Project

Contemporary engineering design is increasingly shaped by the need to address a broad, often competing set of objectives, with a major focus on sustainability and environmental impact across the full life cycle of engineered systems. Despite these demands, dominant approaches to engineering design continue to emphasise incremental modification and component-level refinement of existing system representations, rather than enabling principled exploration of alternative system architectures under tightly coupled objectives. In response, recent computational approaches have begun to integrate artificial intelligence and machine learning into the design process. Deep generative models have demonstrated effectiveness in exploring high-dimensional design spaces and optimising parameters within predefined system representations. However, these methods remain constrained by fixed parameterisations, limiting their ability to support open-ended synthesis and the generation of novel designs. Although large language models (LLMs) show strong capabilities in code and specification generation, they still currently operate as open-loop components in engineering design contexts, limiting the ability to ensure geometric consistency or physical validity of the output. This project aims to overcome these challenges, integrating agentic AI as an orchestrator of an open-ended design generator combined with analysis and simulation tools to inform the design.

Key contributions expected:

• a literature review on both the history as well as the state-of-the-art of computational methods for design (design grammars, generative design, data-driven/machine learning-based design)
• application of agentic LLMs to combinations of existing design representations (design grammars, topology optimisation parametrisations) and simulation/analysis tools
• development of generalised design representations that can leverage LLM latent domain knowledge, as well as support for structured interaction with simulation/analysis tools
• selecting relevant case studies in a relevant domain of study and benchmarking against existing methods on performance and sustainability metrics such as embodied carbon, resource depletion or other impact factors

Funding Notes

This PhD project is funded by the John Anderson Research Studentship Scheme (JARSS) International Strategic Partnership (ISP). It covers UK home tuition fees and an annual tax-free stipend. Additional funding may be available to cover travel to conferences and academic events, software, and equipment costs.