AI-Based Modeling and Simulation for Complex Cyber-Physical Systems
Keywords:
Cyber-physical systems; artificial intelligence; modeling and simulation; digital twins; system architecture; data governance; robustness; fairness; policyAbstract
The convergence of artificial intelligence with cyber-physical systems has opened a transformative frontier for modeling and simulation across critical infrastructures, manufacturing, transportation, and healthcare. This paper presents a system-level examination of AI-based modeling and simulation paradigms for complex cyber-physical systems, moving beyond algorithm-centric perspectives to engage with structural trade-offs, architectural choices, governance, and long-term sustainability. The discussion begins by delineating the foundational characteristics of cyber-physical systems that challenge conventional simulation approaches, emphasizing heterogeneity, real-time coupling, and emergent behaviors. Architectural paradigms ranging from digital twin frameworks to hybrid model-based and data-driven compositions are analyzed in terms of scalability, composability, and their implications for verification and validation. Data infrastructure and interoperability are explored as enablers of fidelity, with attention to streaming data lifecycles, cross-organizational governance, and the tension between data abundance and data quality. Deployment and robustness concerns are addressed through the lens of edge-cloud continuum design, fault resilience, and the hidden costs of retraining in evolving physical environments. The paper further examines fairness, safety, and policy dimensions, arguing that AI integration in cyber-physical simulation recasts regulatory responsibility and demands new auditability mechanisms. Finally, forward-looking perspectives link emerging scientific machine learning techniques with sociotechnical governance frameworks, underscoring the necessity of interdisciplinary collaboration for responsible innovation. The analysis synthesizes insights from systems engineering, machine learning, distributed computing, and science and technology studies to offer a holistic research agenda for next-generation AI-augmented cyber-physical simulations.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
