# Augmented Computational Design: Methodical Application of Artificial Intelligence in Generative Design

arXiv: https://arxiv.org/abs/2310.09243 · HTML: https://mlsanity.com/paper/2310.09243/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 49 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Deep Generative Models in Engineering Design: A Review](https://mlsanity.com/paper/2110.10863/) (2021)
- [MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training](https://mlsanity.com/paper/2106.05630/) (2021)
- [Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design](https://mlsanity.com/paper/2206.07170/) (2022)

## Abstract (arXiv)

This chapter presents methodological reflections on the necessity and utility of artificial intelligence in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to deliver in terms of a few outcomes of interest or performance indicators while dealing with hundreds or thousands of small decisions. The core of the performance-based generative design paradigm is about making statistical or simulation-driven associations between these choices and consequences for mapping and navigating such a complex decision space. This chapter will discuss promising directions in Artificial Intelligence for augmenting decision-making processes in architectural design for mapping and navigating complex design spaces.