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

arXiv: https://arxiv.org/abs/2606.28126 · HTML: https://mlsanity.com/paper/2606.28126/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 26 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [Spatial Optimization of Interconnected Systems in Non-Convex Design Spaces](https://mlsanity.com/paper/2605.17387/) (2026)
- [A Hybrid Optimization Framework for Spatial Packaging of Interconnected Systems](https://mlsanity.com/paper/2605.17424/) (2026)

## Abstract (arXiv)

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.