AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture

2025-06-06

AI Summary

Subsymbolic AI struggles with training under few-shot and low-quality data, while existing virtual simulation approaches lack systematic, standards-aligned frameworks. Method: We conduct a systematic literature review covering 22 state-of-the-art works and propose, for the first time, a unified reference framework for digital twin–driven AI simulation—deeply integrating digital twins with AI agents to establish a closed-loop, cyber-physical data orchestration mechanism. We further achieve systematic alignment of this framework with the ISO 23247 international standard for digital twins. Contribution/Results: We distill key technological evolution trends, identify five core challenges and several open research directions, and deliver a reusable architectural guideline and reference framework. This work provides a standardized, methodology-driven foundation for high-fidelity AI simulation, advancing both theoretical rigor and practical deployability in industrial AI applications.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Intelligent Robots: Embodied AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.

Problem

• Addressing insufficient data for subsymbolic AI adoption
• Exploring digital twins for AI simulation and training
• Providing architectural guidelines for digital twin-enabled AI

Innovation

• Digital twins enable high-fidelity AI simulation
• Virtual training environments generate synthetic data
• ISO 23247 architecture guides digital twin integration