Transformative Synergies of Data-Driven and Generative AI Techniques in ‎‎Modeling and Simulation of Complex Systems

Dear Colleagues,

Integrating data-driven techniques with various modeling approaches offers a powerful means to systems thinking and how we study, understand, and simulate the intricate behaviors of complex systems. With computing resources more accessible than ever, agent-based simulation and the creation of digital twins have risen during the past decade. In addition, the rise of generative AI has opened new frontiers in this realm. Agentic AI, referring to AI systems with autonomous decision-making capabilities, adds a layer of intelligence and adaptability to simulation models and allows for more dynamic and realistic simulations, particularly in environments where complex interactions and evolving conditions are present. By integrating these capabilities with innovative simulation modeling techniques, simulation models' accuracy, adaptability, and predictive power can be enhanced, leading to deeper insights and more robust solutions for complex problems.

This Special Issue aims to explore innovative methods that leverage real-world data to enhance simulations' accuracy and predictive capabilities. By focusing on data-driven approaches and integration of generative AI with various simulation modeling approaches, including agent-based modeling, discrete event methods, system dynamics, and hybrid simulations, we can achieve a deeper understanding of complex systems across various domains, such as social systems, ecological environments, economic models, and urban planning.

This Special Issue invites scientific contributions that propose approaches for combining data analytics and applying machine learning techniques to the modeling and simulation of complex systems with diverse simulation modeling techniques. We encourage submissions that address theoretical advancements, practical applications, and case studies that demonstrate the effectiveness of these methods in real-world scenarios. This Special Issue aims to provide a platform for academics and practitioners to share their insights and findings, fostering collaboration and knowledge dissemination in complex systems modeling and simulation.

We particularly welcome articles presenting, among other aspects:

• Development and validation of data-driven simulation models.
• Applications of machine learning techniques in social, ecological, economic, and urban systems simulation models.
• Case studies demonstrating the use of different simulation model techniques in combination with machine learning approaches in providing insights for understanding complex problems.
• Comparative studies of traditional vs. data-driven and AI-driven simulation modeling approaches.
• Methodologies for integrating real-world data into simulation models.

Dr. Mahdi Najafabadi
Prof. Dr. Ali Asgary
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data-driven simulation
• agent-based modeling (ABM)
• multi-agent systems
• discrete event simulation (DES)
• system dynamics
• hybrid simulation
• digital twins
• machine learning
• agentic AI