About this Research Topic
The Ising model has been an essential paradigm of Statistical Physics to study phase transitions and critical phenomena in various systems. Due to the versatile nature of this theoretical framework, recently, there has been a surge of interest in applying Ising-like models to diverse types of complex systems defined in the broadest possible sense. Given that the original Ising model was proposed by Lenz in 1920, approximately 100 years ago, and then solved by Ising in 1925, this issue can be considered a highlight of the Ising-type models. The issue will cover research capturing essential physics related to Ising-inspired models in various fields, bridging the gap between microscopic and macroscopic descriptions of complex systems.
This issue will bring out the critique of Ising-inspired modeling in modern research on complex systems, which are characterized by many interacting components leading to emergent collective behavior and patterns. We aim to summarize insights on the non-equilibrium dynamics and topological aspects of complex systems gained via multi-scale modeling and the role of the Ising model in developing new Monte Carlo methods, machine learning algorithms, and optimization techniques. The goal is to emphasize how Ising-inspired modeling can have practical impacts in bioengineering, network design, artificial intelligence, and public policy and planning.
We welcome full-length or mini reviews and research articles from researchers whose work relies on applying Ising-inspired agent-based models in complex systems, such as phase transitions and pattern formation in quantum and classical physics, Active and Soft matter, biological physics, neuroscience, social sciences, economics, computer and data sciences, and many others. Some specific examples include but are not limited to, variants of the Ising model like Lattice gas models, Potts models, Cellular Potts models, Ising-type models of social networks and financial markets, Ising-type models of game theory, Kinetic Ising model, Bayesian Ising model, and development of Ising-type algorithms in data sciences.
This issue will bring out the critique of Ising-inspired modeling in modern research on complex systems, which are characterized by many interacting components leading to emergent collective behavior and patterns. We aim to summarize insights on the non-equilibrium dynamics and topological aspects of complex systems gained via multi-scale modeling and the role of the Ising model in developing new Monte Carlo methods, machine learning algorithms, and optimization techniques. The goal is to emphasize how Ising-inspired modeling can have practical impacts in bioengineering, network design, artificial intelligence, and public policy and planning.
We welcome full-length or mini reviews and research articles from researchers whose work relies on applying Ising-inspired agent-based models in complex systems, such as phase transitions and pattern formation in quantum and classical physics, Active and Soft matter, biological physics, neuroscience, social sciences, economics, computer and data sciences, and many others. Some specific examples include but are not limited to, variants of the Ising model like Lattice gas models, Potts models, Cellular Potts models, Ising-type models of social networks and financial markets, Ising-type models of game theory, Kinetic Ising model, Bayesian Ising model, and development of Ising-type algorithms in data sciences.
Keywords: Complex systems, Ising model variants, Agent based models, Monte Carlo method, Pattern formation, Collective behavior, Social networks, Data sciences, Biological physics, Soft matter physics, Critical phenomena
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
