# Artificial Intelligence and Modeling & Simulation: An Overview

arXiv: https://arxiv.org/abs/2608.00366 · HTML: https://mlsanity.com/paper/2608.00366/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 125 references that are in the map.
- First posted on arXiv 1 Aug 2026.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia](https://mlsanity.com/paper/2312.03664/) (2023)
- [Transformers for Modeling Physical Systems](https://mlsanity.com/paper/2010.03957/) (2020)
- [Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data](https://mlsanity.com/paper/2402.17644/) (2024)
- [Safe Reinforcement Learning on Autonomous Vehicles](https://mlsanity.com/paper/1910.00399/) (2019)
- [RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios](https://mlsanity.com/paper/2312.13303/) (2023)
- [S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation](https://mlsanity.com/paper/2402.02112/) (2024)
- [Agent-Based Modelling Meets Generative AI in Social Network Simulations](https://mlsanity.com/paper/2411.16031/) (2024)
- [Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat](https://mlsanity.com/paper/2401.17163/) (2024)
- [GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Tasks](https://mlsanity.com/paper/2306.13679/) (2023)
- [Data Cleaning Using Large Language Models](https://mlsanity.com/paper/2410.15547/) (2024)
- [Breaking Barriers or Building Dependency? Exploring Team-LLM Collaboration in AI-infused Classroom Debate](https://mlsanity.com/paper/2501.09165/) (2025)
- [AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture](https://mlsanity.com/paper/2506.06580/) (2025)
- [Digital Twin Evolution for Sustainable Smart Ecosystems](https://mlsanity.com/paper/2403.07162/) (2024)
- [Metadata practices for simulation workflows](https://mlsanity.com/paper/2408.17309/) (2024)
- [Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities](https://mlsanity.com/paper/2409.15290/) (2024)
- [Introduction to Digital Twins for the Smart Grid](https://mlsanity.com/paper/2602.14256/) (2026)
- [Unlocking the Potential of Past Research: Using Generative AI to Reconstruct Healthcare Simulation Models](https://mlsanity.com/paper/2503.21646/) (2025)
- [BPMN Assistant: An LLM-Based Approach to Business Process Modeling](https://mlsanity.com/paper/2509.24592/) (2025)
- [Enhanced Input Modeling for Construction Simulation using Bayesian Deep Neural Networks](https://mlsanity.com/paper/1906.06421/) (2019)
- [Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns](https://mlsanity.com/paper/2109.06776/) (2021)
- [How Well Can AI Build SD Models?](https://mlsanity.com/paper/2503.15580/) (2025)
- [Lost in the Pipeline: How Well Do Large Language Models Handle Data Preparation?](https://mlsanity.com/paper/2511.21708/) (2025)
- [Generative AI in Simulation-Based Test Environments for Large-Scale Cyber-Physical Systems: An Industrial Study](https://mlsanity.com/paper/2512.05507/) (2025)

## Abstract (arXiv)

Artificial intelligence (AI) and Modeling & Simulation (M&S) are increasingly intertwined, reflecting converging research needs across both communities, rapid technological advances such as the rise of generative AI, and the growing availability of data and computational resources. This report provides a structured overview of the intersections of AI and M&S. The relationship goes both ways: AI can support, augment, or even replace components of simulation studies, while simulations can serve as data generators, training environments, and evaluation platforms for AI. We organize this landscape along the stages of M&S from model specification and input modeling to execution, experimentation, verification and validation, and output analysis. Selected studies at each stage illustrates how techniques such as Large Language Models have reshaped simulation practices, while highlighting limitations and open challenges. This report also provides a conceptual roadmap that helps readers navigate a rapidly changing ecosystem.