Artificial Intelligence and Modeling & Simulation: An Overview

• URL: http://arxiv.org/abs/2608.00366v1
• Date: Sat, 01 Aug 2026 00:36:44 GMT
• Title: Artificial Intelligence and Modeling & Simulation: An Overview
• Abstract summary: Artificial intelligence (AI) and Modeling & Simulation (M&S) are increasingly intertwined.<n>This report provides a structured overview of the intersections of AI and M&S.
• Score: 2.982218441172364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making [0.9236873716369859]
  Surrogate models play a central role in reducing the computational cost of complex systems simulations.<n>Explainable Artificial Intelligence (XAI) offers powerful tools to unpack these models.<n>XAI methods struggle with engineering-specific constraints, such as highly inputs, dynamical systems, and rigorous reliability requirements.
  arXiv  Detail & Related papers  (2026-04-15T03:25:10Z)
• Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic [7.44360671272726]
  We introduce a taxonomy that organizes methods into three families: agent-level behavior models, environment-level simulation methods, and cognitive and physics-informed methods.<n>By covering both traffic engineering and computer science perspectives, we aim to bridge the gap between these two communities.
  arXiv  Detail & Related papers  (2026-04-14T15:09:07Z)
• Developing AI Agents with Simulated Data: Why, what, and how? [9.087189607749094]
  This chapter introduces the reader to the key concepts, benefits, and challenges of simulation-based synthetic data generation for AI training purposes.<n>It describes, design, and analyze digital twin-based AI simulation solutions.
  arXiv  Detail & Related papers  (2026-02-17T18:53:27Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• Generative Physical AI in Vision: A Survey [78.07014292304373]
  Gene Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication.<n>This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content.<n>As generative models evolve to increasingly integrate physical realism and dynamic simulation, their potential to function as "world simulators" expands.
  arXiv  Detail & Related papers  (2025-01-19T03:19:47Z)
• Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey [49.29751866761522]
  This paper aims to investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field. Then, an overview of the latest GenAI models is systematically reviewed. Finally, the corresponding applications in SAR domain are also included.
  arXiv  Detail & Related papers  (2024-11-05T03:06:00Z)
• Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation [1.6230958216521798]
  This study investigates the potential of leveraging the pre-trained Large Language Models (LLMs) By adopting ChatGPT API as the reasoning core, we outline an integrated workflow that encompasses natural language processing, methontology-based prompt tuning, and transformers. The outcomes of our methodology are knowledge graphs in widely adopted ontology languages (e.g., OWL, RDF, SPARQL)
  arXiv  Detail & Related papers  (2024-05-29T16:40:31Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• RoboTHOR: An Open Simulation-to-Real Embodied AI Platform [56.50243383294621]
  We introduce RoboTHOR to democratize research in interactive and embodied visual AI. We show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs.
  arXiv  Detail & Related papers  (2020-04-14T20:52:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.