This Research Topic discusses the latest developments in complex systems research and intends to give exposure to prospective readers about the data-driven approaches and modeling aspects of distinct complex physical, biological, and social systems. To address these issues, different modeling techniques e.g., deterministic, stochastic, and agent-based models, graph and network theory models, and optimization models will be employed to examine the complex systems of interest. Additionally, data-driven techniques e.g., machine learning, neural networks, persistent homology, and big data analytics will also be discussed in greater detail so as to provide another paradigm on how data analysis and data interpretation are being used in complex systems research. Special attention is given to various applications on the techniques of complex systems in examining the social issues, epidemiology, ecological and environmental problems, engineering and industrial applications. Many realistic examples from recent research are also employed in this volume as illustrations.
The main purpose of this article collection is to emphasize a unified approach to complex systems analysis, which goes beyond examining complicated phenomena of numerous real-life systems; this is done by investigating a huge number of components that interact with each other at different (microscopic and macroscopic) scales; new insights and emergent collective behaviors can evolve from the interactions between individual components and also with their environments. These tools and concepts will allow us to better understand the patterns of various real-life systems and help us comprehend the mechanisms behind which distinct factors shape some complex systems phenomena. As mentioned above, this volume is specially designed to take into account a multidisciplinary approach in complex systems analysis that will encourage the transfer of ideas and methodology from data-driven techniques and modeling fields to the other areas of knowledge (and vice versa).
"Advances in Data-Driven Approaches and Modeling of Complex Systems" is a medium for scientific communication and dissemination of ideas on the advancements being made in the application of the complex systems to physical, biological, and social science fields. This Research Topic encourages the exchange of important research, instruction, ideas and information on all aspects of the rapidly expanding area of complex systems. The main focus areas (or research themes) of this article collection include (but is not limited to):
i) deterministic modeling,
ii) stochastic modeling,
iii) agent-based models,
iv) models of graph and network theory,
v) optimization of discrete and continuous models,
vi) machine learning,
vii) neural networks,
viii) persistent homology,
ix) big data analytics.
We accept a wide range of manuscript types e.g., original research, review, brief research report, general commentary, opinion and new discovery.
This Research Topic discusses the latest developments in complex systems research and intends to give exposure to prospective readers about the data-driven approaches and modeling aspects of distinct complex physical, biological, and social systems. To address these issues, different modeling techniques e.g., deterministic, stochastic, and agent-based models, graph and network theory models, and optimization models will be employed to examine the complex systems of interest. Additionally, data-driven techniques e.g., machine learning, neural networks, persistent homology, and big data analytics will also be discussed in greater detail so as to provide another paradigm on how data analysis and data interpretation are being used in complex systems research. Special attention is given to various applications on the techniques of complex systems in examining the social issues, epidemiology, ecological and environmental problems, engineering and industrial applications. Many realistic examples from recent research are also employed in this volume as illustrations.
The main purpose of this article collection is to emphasize a unified approach to complex systems analysis, which goes beyond examining complicated phenomena of numerous real-life systems; this is done by investigating a huge number of components that interact with each other at different (microscopic and macroscopic) scales; new insights and emergent collective behaviors can evolve from the interactions between individual components and also with their environments. These tools and concepts will allow us to better understand the patterns of various real-life systems and help us comprehend the mechanisms behind which distinct factors shape some complex systems phenomena. As mentioned above, this volume is specially designed to take into account a multidisciplinary approach in complex systems analysis that will encourage the transfer of ideas and methodology from data-driven techniques and modeling fields to the other areas of knowledge (and vice versa).
"Advances in Data-Driven Approaches and Modeling of Complex Systems" is a medium for scientific communication and dissemination of ideas on the advancements being made in the application of the complex systems to physical, biological, and social science fields. This Research Topic encourages the exchange of important research, instruction, ideas and information on all aspects of the rapidly expanding area of complex systems. The main focus areas (or research themes) of this article collection include (but is not limited to):
i) deterministic modeling,
ii) stochastic modeling,
iii) agent-based models,
iv) models of graph and network theory,
v) optimization of discrete and continuous models,
vi) machine learning,
vii) neural networks,
viii) persistent homology,
ix) big data analytics.
We accept a wide range of manuscript types e.g., original research, review, brief research report, general commentary, opinion and new discovery.