AI-Driven Synthesis for High-Tech System Design: Automating Innovation

Researchers propose automation-in-design (AiD) using deep learning and generative AI to automate high-tech system design, demonstrated through e-drive and spatial dimensioning case studies. The framework aims to shift engineering from simulation-based optimization to autonomous design with minimal human supervision.

Computer Science Artificial Intelligence Submitted on 26 Jun 2026 Title:AI-Driven Synthesis for High-Tech System Design: Automating Innovation View PDF /pdf/2606.28126 HTML experimental https://arxiv.org/html/2606.28126v1 Abstract:This article addresses the combinatorial complexity inherent in modern high-tech system design by presenting automation-in-design AiD as a transformative paradigm. We propose computational design synthesis CDS , a framework utilising deep learning and generative AI to automate the creation of novel systems. Two case studies e-drive system design and spatial dimensioning problem serve as proof-points for this approach. The AI-driven methods used in the case studies represent a fundamental shift in engineering, advancing from simulation-based optimisation towards autonomous design with minimal human supervision. Current browse context: cs.AI References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .