Research in the Mathematical Sciences

Scope & Guideline

Empowering Scholars Through Open Access Mathematics

Introduction

Delve into the academic richness of Research in the Mathematical Sciences 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
ISSN2522-0144
PublisherSPRINGER INT PUBL AG
Support Open AccessNo
CountrySwitzerland
TypeJournal
Convergefrom 2014 to 2024
AbbreviationRES MATH SCI / Res. Math. Sci.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressGEWERBESTRASSE 11, CHAM CH-6330, SWITZERLAND

Aims and Scopes

The journal 'Research in the Mathematical Sciences' serves as a platform for high-quality research across various branches of mathematics, promoting innovative methodologies and theoretical advancements. Its core focus encompasses a wide range of mathematical disciplines, emphasizing both foundational theories and their applications.
  1. Algebra and Number Theory:
    Explores theoretical frameworks and applications of algebraic structures, including group theory, ring theory, and number theory, particularly in relation to modular forms and algebraic geometry.
  2. Geometry and Topology:
    Investigates properties and invariants of geometric objects and spaces, focusing on differential geometry, algebraic topology, and related fields, including the study of singularities and manifold structures.
  3. Mathematical Analysis and Partial Differential Equations (PDEs):
    Covers topics in real and complex analysis, including functional analysis and the theory of PDEs, with a focus on existence, uniqueness, and stability of solutions.
  4. Combinatorics and Discrete Mathematics:
    Addresses combinatorial structures, graph theory, and their applications in various mathematical contexts, including Ramsey theory and enumeration problems.
  5. Mathematical Physics and Applied Mathematics:
    Integrates mathematical theories with physical applications, exploring areas such as quantum mechanics, statistical mechanics, and mathematical modeling in various scientific fields.
  6. Computational Mathematics and Algorithm Design:
    Focuses on numerical methods, algorithms, and their applications in solving complex mathematical problems, including optimization and machine learning techniques.
  7. Tropical Geometry and Algebraic Geometry:
    Studies the interplay between combinatorial properties and algebraic structures, particularly through the lens of tropical geometry and its applications to classical algebraic geometry.
  8. Singularity Theory and Its Applications:
    Examines the role of singularities in various mathematical contexts, including algebraic geometry and differential topology, emphasizing their theoretical and practical implications.
Recent publications indicate a dynamic shift towards emerging themes that reflect current trends in mathematical research. These themes align with interdisciplinary approaches and the application of advanced computational techniques.
  1. Machine Learning and Neural Networks:
    A significant trend towards integrating machine learning techniques, particularly neural networks, into mathematical research, highlighting their applications in optimization and data analysis.
  2. Quantum Mathematics and Modular Forms:
    Emerging research on quantum modular forms and their implications in number theory and mathematical physics points to a growing interest in the intersection of these fields.
  3. Tropical Geometry Applications:
    Increasing focus on tropical geometry and its applications in algebraic geometry and combinatorics illustrates a shift towards innovative methodologies in these areas.
  4. Nonlinear Dynamics and Chaos Theory:
    An uptick in studies addressing nonlinear dynamics, chaos theory, and their mathematical modeling reflects an increasing recognition of their importance in both theoretical and applied contexts.
  5. Complex Systems and Network Theory:
    Research that explores complex systems and network theory, especially in relation to mathematics applied to social and biological systems, is gaining traction, indicating a broader interdisciplinary focus.
  6. Singularity Theory and Applications:
    A renewed interest in singularity theory, particularly its applications in geometry and topology, suggests a vibrant area of research that is evolving and expanding.
  7. Mathematical Biology and Epidemiology:
    The application of mathematical techniques to biological problems, particularly in modeling epidemics and population dynamics, shows a rising trend in the journal's publications.

Declining or Waning

As the journal evolves, certain themes appear to be losing prominence. This decline may reflect shifts in research interests or the maturation of certain fields within mathematics.
  1. Classical Real Analysis:
    While foundational, traditional topics in real analysis have seen a decrease in focus, possibly due to the rise of more applied and computational approaches that address complex problems.
  2. Elementary Number Theory:
    The exploration of elementary number theory concepts has diminished, as more advanced and abstract approaches dominate the discourse in number theory, particularly in relation to modular forms.
  3. Graph Theory Basics:
    Basic studies in graph theory are becoming less frequent, with more emphasis now placed on specific applications and advanced topics such as topological graph theory or algebraic graph theory.
  4. Traditional Differential Equations:
    Research focusing solely on classical differential equations without modern applications or computational methods has shown a decline, as interest shifts towards more complex and applied differential systems.
  5. Deterministic Mathematical Models:
    The focus on purely deterministic models is waning in favor of stochastic models and machine learning approaches that incorporate randomness and uncertainty in mathematical modeling.

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