Communications in Nonlinear Science and Numerical Simulation

Scope & Guideline

Empowering Discoveries in Applied Mathematics and Simulation

Introduction

Delve into the academic richness of Communications in Nonlinear Science and Numerical Simulation with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1007-5704
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1996 to 2025
AbbreviationCOMMUN NONLINEAR SCI / Commun. Nonlinear Sci. Numer. Simul.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal "Communications in Nonlinear Science and Numerical Simulation" focuses on the development and application of numerical methods and nonlinear science across a variety of fields including mathematics, physics, engineering, and biology. The journal aims to publish high-quality research that advances the understanding of nonlinear phenomena and provides innovative numerical techniques for their analysis.
  1. Nonlinear Dynamics and Control:
    Research on the dynamics of nonlinear systems, including stability, bifurcations, and control strategies for complex systems.
  2. Numerical Methods and Simulations:
    Development and application of numerical methods for solving nonlinear partial differential equations, including finite element methods, spectral methods, and meshless approaches.
  3. Mathematical Modeling:
    Creation and analysis of mathematical models for various applications in physics, biology, engineering, and finance, particularly focusing on models that exhibit nonlinear behavior.
  4. Stochastic Systems and Analysis:
    Study of stochastic processes and their applications in modeling uncertainty and randomness in complex systems.
  5. Epidemiological Models:
    Modeling and analysis of the dynamics of infectious diseases, including the impact of vaccination and other control measures.
  6. Emerging Technologies and Applications:
    Exploration of nonlinear phenomena in emerging technologies, such as energy harvesting, smart materials, and biomedical applications.
The journal is currently experiencing a surge in interest in several emerging themes and methodologies that reflect the latest developments in nonlinear science and numerical simulation. These trends highlight the journal's commitment to staying at the forefront of research in these dynamic fields.
  1. Machine Learning and AI in Nonlinear Dynamics:
    There is an increasing trend towards integrating machine learning techniques with nonlinear dynamics to enhance prediction, control, and analysis of complex systems.
  2. Fractional Calculus Applications:
    Research on fractional calculus and its applications in modeling real-world phenomena is gaining momentum, reflecting its growing recognition in both theoretical and applied contexts.
  3. Complex Network Dynamics:
    A rise in studies focused on the dynamics of complex networks, particularly in relation to synchronization, stability, and information diffusion, indicates a growing interdisciplinary interest.
  4. Epidemiological Modeling with Nonlinear Dynamics:
    The pandemic has propelled a surge in research related to epidemiological models, particularly those incorporating nonlinear dynamics to better understand disease spread and control strategies.
  5. Nonlinear Energy Harvesting:
    Emerging interest in nonlinear energy harvesting techniques, particularly in the context of smart materials and devices, is becoming a prominent research focus.

Declining or Waning

While the journal has maintained a broad focus on nonlinear science and numerical simulations, certain themes have seen a decline in prominence over recent years. This may reflect changing research priorities or advancements in specific methodologies that have shifted the focus of the community.
  1. Classical Mechanics Applications:
    There has been a noticeable decrease in papers focused specifically on classical mechanics applications of nonlinear dynamics, potentially due to a shift towards more interdisciplinary applications.
  2. Traditional Numerical Methods:
    The prevalence of traditional numerical methods appears to be waning as newer, more sophisticated techniques such as machine learning and advanced computational methods gain traction.
  3. Purely Theoretical Nonlinear Analysis:
    Research that focuses solely on theoretical aspects of nonlinear analysis without practical applications has seen reduced attention, as applied research becomes more favored.

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