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

2026-06-26

AI Summary

This study addresses the combinatorial explosion inherent in modern high-tech system design by proposing a Computational Design Synthesis (CDS) framework that integrates deep learning and generative artificial intelligence to establish a new paradigm of Artificial intelligence–driven Design (AiD). The framework enables autonomous generation and innovation of highly complex engineering systems, shifting the design paradigm from simulation-based optimization toward minimally supervised, self-directed synthesis. Experimental validation in two representative scenarios—electric drive system design and spatial layout planning—demonstrates that CDS substantially reduces the need for human intervention while significantly enhancing both the efficiency and feasibility of innovative design outcomes.

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.

Problem

• combinatorial complexity
• high-tech system design
• computational design synthesis
• automation-in-design
• autonomous design

Innovation

• computational design synthesis
• generative AI
• automation-in-design
• autonomous design
• deep learning