ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS

Scope & Guideline

Shaping the Future of Optimization and Control

Introduction

Delve into the academic richness of ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS 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
ISSN1292-8119
PublisherEDP SCIENCES S A
Support Open AccessNo
CountryFrance
TypeJournal
Convergefrom 1996 to 2024
AbbreviationESAIM CONTR OPTIM CA / ESAIM-Control OPtim. Calc. Var.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address17, AVE DU HOGGAR, PA COURTABOEUF, BP 112, F-91944 LES ULIS CEDEX A, FRANCE

Aims and Scopes

ESAIM: Control, Optimisation and Calculus of Variations focuses on advancing the theoretical and practical aspects of control theory, optimization, and calculus of variations. The journal publishes high-quality research that bridges these interconnected domains, emphasizing mathematical rigor and innovative methodologies.
  1. Control Theory:
    Research in this area covers the mathematical and algorithmic foundations of control systems, including linear and nonlinear systems, optimal control, and feedback stabilization.
  2. Optimization Techniques:
    The journal emphasizes various optimization methodologies, such as stochastic optimization, convex optimization, and optimal control problems, particularly in relation to partial differential equations (PDEs) and dynamic systems.
  3. Calculus of Variations:
    Papers often explore variational principles and techniques, addressing problems of finding extrema of functionals, particularly in infinite-dimensional spaces.
  4. Stochastic Processes and Games:
    The journal includes research on stochastic processes, mean-field games, and their applications in economics, finance, and engineering.
  5. Numerical Methods and Computational Approaches:
    Focus on the development and analysis of numerical algorithms for solving control and optimization problems, including finite element methods and approximation techniques.
  6. Applications in Physical and Biological Systems:
    Many papers apply control and optimization theories to real-world problems, including fluid dynamics, biological systems, and material sciences.
The journal has identified several emerging themes that are gaining prominence, reflecting the evolving landscape of control theory and optimization research.
  1. Data-Driven Control and Learning-Based Approaches:
    There is a growing trend towards integrating machine learning and data-driven methods into control strategies, particularly in adaptive and reinforcement learning contexts.
  2. Stochastic and Robust Control:
    Research focusing on stochastic control problems, including robust methods that handle uncertainty and variability in system dynamics, is increasingly prevalent.
  3. Mean-Field Games and Collective Dynamics:
    The study of mean-field games, which model interactions in large populations, has emerged as a significant area of interest, bridging control theory with game theory and statistical mechanics.
  4. Optimal Transport and Variational Methods:
    An increase in papers exploring optimal transport theory and its applications to variational problems signals a growing intersection between these fields and traditional control problems.
  5. Multi-Agent Systems and Distributed Control:
    Research on distributed control strategies for multi-agent systems is gaining traction, reflecting the need for coordinated control in complex networks and decentralized systems.
  6. Applications in Emerging Technologies:
    There is an increasing focus on applications of control and optimization in emerging technologies, such as robotics, autonomous systems, and smart grids, indicating a shift towards real-world impact.

Declining or Waning

While ESAIM: Control, Optimisation and Calculus of Variations continues to explore a wide range of themes, some areas have seen a decline in publication frequency, reflecting shifting research interests.
  1. Classical Control Methods:
    There has been a noticeable decline in papers focusing on classical control methods, such as PID control and basic feedback mechanisms, as researchers increasingly explore more complex and adaptive strategies.
  2. Static Optimization Problems:
    Research centered around static optimization problems has waned, with a shift toward dynamic and time-dependent optimization scenarios that reflect real-time applications.
  3. Purely Theoretical Studies:
    Papers that focus solely on theoretical aspects without practical applications or numerical methods have decreased, indicating a trend towards applied research that can demonstrate real-world relevance.
  4. Traditional Game Theory:
    Interest in classical game theory applications has declined in favor of more complex models involving stochastic or mean-field dynamics, which better capture contemporary challenges in economics and social sciences.

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