Journal of Physics-Complexity

Scope & Guideline

Pioneering Open Access Research in Complexity and AI

Introduction

Immerse yourself in the scholarly insights of Journal of Physics-Complexity with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN-
PublisherIOP Publishing Ltd
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationJ PHYS-COMPLEXITY / J. Phys.-Complex.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressTEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND

Aims and Scopes

The Journal of Physics-Complexity focuses on the intricate behaviors and interactions within complex systems across various fields, employing a range of quantitative methods and theoretical frameworks. The journal aims to provide a platform for innovative research that sheds light on the underlying principles governing complex phenomena.
  1. Complex Networks and Graph Theory:
    Research on the structure and dynamics of complex networks, utilizing graph theory to understand connectivity, community detection, and network robustness.
  2. Statistical Physics in Complex Systems:
    Application of statistical physics principles to study emergent behaviors in complex systems, including phase transitions and critical phenomena.
  3. Dynamical Systems and Chaos:
    Investigation of chaotic dynamics, bifurcations, and stability in various systems, with a focus on understanding transient behaviors and synchronization.
  4. Modeling and Simulation Techniques:
    Development of new modeling approaches, including agent-based models and computational simulations, to explore complex interactions in socio-economic, ecological, and technological systems.
  5. Interdisciplinary Applications:
    Integration of concepts from physics, biology, social sciences, and economics to address complex challenges, demonstrating the journal's commitment to cross-disciplinary research.
  6. Machine Learning and Data Science:
    Utilization of machine learning techniques to analyze complex datasets, enhance predictive modeling, and improve understanding of complex interactions.
The Journal of Physics-Complexity has been at the forefront of exploring new and innovative themes that reflect the current interests and advancements in the field of complex systems. This section highlights recent trends and emerging topics that are gaining traction within the journal.
  1. Complex Adaptive Systems:
    Research increasingly focuses on complex adaptive systems, exploring how agents interact and adapt within dynamic environments, which is crucial for understanding ecological and social phenomena.
  2. Resilience and Robustness in Networks:
    There is a growing emphasis on studying resilience and robustness in complex networks, particularly in response to external perturbations and failures, which is vital for infrastructure and ecological sustainability.
  3. Epidemiological Modeling:
    The journal has seen a surge in studies related to epidemiological modeling, especially in light of global health challenges, demonstrating the relevance of complex systems theory in understanding disease dynamics.
  4. Data-Driven Approaches:
    Emerging themes include the application of data-driven methods, such as machine learning and AI, to uncover patterns and insights in complex systems, enhancing predictive capabilities and understanding.
  5. Interconnected Systems and Globalization:
    Research focusing on the interconnectedness of global systems, including economic and environmental networks, is trending, reflecting the complexities of globalization and its impacts.

Declining or Waning

As the Journal of Physics-Complexity evolves, certain areas of research appear to be diminishing in prominence. This section identifies themes that have seen a decline in publication frequency or relevance within the journal's recent issues.
  1. Traditional Linear Models:
    There has been a noticeable decline in research focused on traditional linear modeling approaches as the journal increasingly emphasizes non-linear dynamics and complex interactions.
  2. Basic Statistical Analysis:
    Research that relies solely on basic statistical methods without incorporating complex system dynamics or network structures has become less frequent, reflecting a shift towards more sophisticated analytical techniques.
  3. Single-Disciplinary Studies:
    Papers focusing exclusively on a single discipline without interdisciplinary connections have waned, indicating a trend towards integrating multiple fields to address complex problems.
  4. Static Network Models:
    Research centered on static representations of networks is declining, with a growing emphasis on dynamic and evolving network structures that better represent real-world complexities.
  5. Overly Simplistic Agent-Based Models:
    Simplistic agent-based models that do not account for significant interactions or complexities are becoming less favored, as the journal seeks more nuanced and detailed modeling frameworks.

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