Manuscript Topics

This special issue aims to bring together new advanced computational methods to model and simulate complex systems. Modeling and simulation is one of the primary approaches to study the behaviors and patterns of complex systems in different domains, such as physics, engineering, biology, environment, ecology, finance, and public policy. Besides the traditional approaches including statistical methods, discrete time models, discrete event specifications, and agent-based modeling, machine learning and deep learning have been widely adopted in the modeling and simulation of complex systems.

This special issue welcomes high quality submissions that focus on both theoretical and practical aspects of modeling and simulation in complex systems, with particular emphasis on machine/deep learning algorithms to model and simulate different applications. We especially encourage contributions that innovatively integrate the machine/deep learning models into the traditional modeling approaches to more effectively address the challenges in complex system modeling. This issue will provide a forum for researchers and practitioners in various disciplines, foster cross-disciplinary collaborations, and advance the state-of-the-art in modeling and simulation and its applications.

Keywords

Modeling and simulation, Complex systems, Machine learning, Deep learning, Artificial Intelligence, Discrete time, Discrete event, Data-driven modeling, Data assimilation, Agent-based modeling

Instructions for authors
https://www.aimspress.com/era/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/