Artificial Intelligence in Modeling and Simulation (2nd Edition)

Dear Colleagues,

Modeling and Simulation (M&S) uses various methods, including continuous, discrete-event, and agent-based approaches, to represent and analyze real-world systems. It is an essential tool in science and engineering, offering alternatives to physical experimentation, which can be costly, time-consuming, or impractical in cases such as inaccessible environments or systems still under development. M&S is widely applied in fields like physics, social sciences, medicine, defense, transportation, chemistry, ecology, and biology, where it helps to predict and understand complex system behaviors without direct experimentation.

Artificial Intelligence (AI) has become increasingly important in M&S, enabling the development of more sophisticated and adaptive models. AI refers to systems that can perceive their environment and perform tasks to achieve specific goals, often replicating human-like intelligence. In M&S, AI is integrated in several ways, for example by embedding AI within simulation models or by using AI to develop and optimize these models. When AI is a component of the real system—such as in autonomous vehicles, smart factories, or social networks—it must be accurately reflected in the simulation models. In other cases, simulations that aim to mimic natural intelligence rely on AI techniques to achieve this.

AI techniques, particularly machine learning, enhance M&S by optimizing and fine-tuning models. These methods enable models to be trained using real system data, improving accuracy and reliability. AI also aids in synchronizing models with real-time data, improving verification and validation processes. One significant advancement is the creation of metamodels, which predict model outcomes across various parameters without requiring full simulations, significantly reducing computational time. This is particularly useful in complex simulations, such as agent-based or multiscale models, where exploring all parameter combinations is computationally intensive.

The interaction between AI and M&S is mutually beneficial. Simulation models can generate synthetic data to train AI systems, which can then be applied in practical contexts. This synergy not only improves the performance of simulations but also drives the advancement of AI methods.

This Special Issue seeks contributions that examine the relationship between AI and M&S, focusing on the following: (1) the application of AI techniques, including machine learning, in M&S across various domains; (2) the development and optimization of simulation models using AI, with a focus on verification and validation; (3) the creation of AI-driven metamodels to enhance simulation efficiency; and (4) the role of simulation-generated data in training AI systems.

We invite submissions addressing these topics from theoretical, algorithmic, and practical perspectives across academic and industrial applications.

Dr. Nuno Fachada
Dr. Nuno David
Guest Editors

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