# Generative Optimization: A Perspective on AI-Enhanced Problem Solving in Engineering

arXiv: https://arxiv.org/abs/2412.13281 · HTML: https://mlsanity.com/paper/2412.13281/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2024.

## Built on by (most cited first, 5 of 5)

- [mPOLICE: Provable Enforcement of Multi-Region Affine Constraints in Deep Neural Networks](https://mlsanity.com/paper/2502.02434/) (2025)
- [Risks of AI-driven product development and strategies for their mitigation](https://mlsanity.com/paper/2506.00047/) (2025)
- [Large Language Models for Combinatorial Optimization of Design Structure Matrix](https://mlsanity.com/paper/2506.09749/) (2025)
- [Benchmarking Generative AI Against Bayesian Optimization for Constrained Multi-Objective Inverse Design](https://mlsanity.com/paper/2511.00070/) (2025)
- [StepCAD: Mesh-to-CAD Code Generation via LLM Policy and Geometry-Guided Search](https://mlsanity.com/paper/2610.03799/) (2026)

## Abstract (arXiv)

The field of engineering is shaped by the tools and methods used to solve problems. Optimization is one such class of powerful, robust, and effective engineering tools proven over decades of use. Within just a few years, generative artificial intelligence (GenAI) has risen as another promising tool for general-purpose problem-solving. While optimization shines at precisely identifying highly-optimal solutions, GenAI excels at inferring problem requirements, bridging solution domains, handling mixed data modalities, and rapidly generating copious numbers of solutions. These differing attributes also make the two frameworks complementary. Hybrid `generative optimization' algorithms have gained traction across a few engineering applications and now comprise an emerging paradigm for engineering problem-solving. We expect significant developments in the near future around generative optimization, leading to changes in how engineers solve problems using computational tools. We offer our perspective on existing methods, areas of promise, and key research questions.