Advances in Difference Equations

Aims & Scope

This journal seeks to publish high quality research and survey articles of exceptional merit in the broad and expansive fields of Applied Mathematics and Data Sciences. The transformative success of these fields stems from groundbreaking theoretical and algorithmic advancements in areas such as scientific computing and machine learning, data-driven modeling, differential equations, numerical analysis, control, optimization, and the development of robust computational tools for managing uncertainty and randomness. Beyond analytical advancements, this journal also welcomes contributions addressing novel computational methodologies and diverse applications, particularly in areas such as Scientific Machine Learning, Mathematical Biology, and Bioengineering. By doing so, the journal aims to serve as a vital bridge between cutting-edge mathematical research and impactful real-world applications. Numerical Analysis and Scientific Computing: Welcoming submissions covering numerical methods for ODEs/PDEs, inverse problems, optimisation and control, model reductions, uncertainty quantification, computational science and engineering, computational mechanics. Partial Differential Equations and Mathematical Physics: This section accepts papers on all aspects of ordinary, partial differential equations and mathematical physics. The section also covers all applications concerning partial differential equations. Control: The section welcomes contributions on the control theory considered in a broad sense, comprising areas of controllability, optimal control, control engineering, inverse problems, etc. The papers must provide novel theoretical and/or application approaches to non-isolated research topics whose relevance has to be justified with clear links to established methods and theories. Stochastic Modeling, Analysis and Uncertainty Quantification: This section accepts research papers on financial markets and population system related stochastic models, analysis of models, incl

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Subject Classification