# Text-to-Image Generative AI for Modeling and Simulation: Methods, Opportunities, and Applications

arXiv: https://arxiv.org/abs/2606.20991 · HTML: https://mlsanity.com/paper/2606.20991/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://mlsanity.com/paper/2302.03668/) (2023)
- [The Chosen One: Consistent Characters in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2311.10093/) (2023)
- [CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models](https://mlsanity.com/paper/2404.15677/) (2024)
- [Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1905.03709/) (2019)
- [CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2603.03281/) (2026)
- [Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis](https://mlsanity.com/paper/2410.21638/) (2024)
- [YOLO26: A Comprehensive Architecture Overview and Key Improvements](https://mlsanity.com/paper/2602.14582/) (2026)

## Built on by (most cited first, 1 of 1)

- [Generative Artificial Intelligence (GenAI) to convert images of queuing networks into verifiable simulation models: an open-weight LLM workflow approach](https://mlsanity.com/paper/2607.24259/) (2026)

## Abstract (arXiv)

Text-to-image generation is a form of generative artificial intelligence (GenAI) that converts textual descriptions into images. Most applications of GenAI in modeling and simulation (M&S) have focused on large language models for documentation, coding, or explanation. By contrast, the potential of image generation remains largely unexplored. This tutorial introduces text-to-image generation to the M&S community and details how it can support several M&S tasks, including communicating conceptual models, visualizing simulation outcomes, generating educational materials, and interfacing heterogeneous models in multi-scale simulations. The tutorial combines conceptual guidance with practical workflows, explaining how modern image generators operate, how prompts and simulation outputs can be translated into visual scenes, and how practitioners can integrate these tools into reproducible local pipelines. By focusing on transferable principles rather than specific tools, the tutorial equips M&S practitioners with the knowledge needed to evaluate, adopt, and adapt text-to-image generation in their simulation workflows.