Journal Guide

The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization.Papers that report on modern materials modeling are of interest, including quantum chemical methods, density functional theory, semi-empirical and classical approaches, statistical mechanics, atomic-scale simulations, mesoscale modeling, phase-field techniques, and finite element methods. Not all topics that potentially fall under the category of computational materials science are appropriate for the journal. For example, submissions that focus on the design of components for structural applications, describe electrical behavior in a device, or characterize thermal or mass transport without extensive accompanying input and associated discussion from computational materials science methods of interest are best suited for other specialized journals.Reports of advances in technical methodologies, and the application of computational materials science to guide, interpret, inspire, or otherwise enhance related experimental materials research are of significant interest as long as the computational methods or results are a primary focus of the manuscript. Contributions on all types of materials systems will be considered in the form of articles, perspectives, and reviews.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

Computational Mathematics and Mathematical Physics is a monthly journal of the Russian Academy of Sciences (RAS). It contains the English translations of papers published in the Zhurnal Vychislitel’noi Matematiki i Matematicheskoi Fiziki, which was founded in 1961 by Academician A. A. Dorodnitsyn. The Journal includes surveys and original papers on computational mathematics, computational methods of mathematical physics, informatics, and other mathematical sciences.

Computational Mathematics and Modeling focuses on important Russian contributions to computational mathematics that are useful to the applied scientist or engineer. This quarterly publication presents timely research articles by scientists from Moscow State University, an institution recognized worldwide for influential contributions to this subject. Numerical analysis, control theory, and the interplay of modeling and computational mathematics are among the featured topics.

Computational Mechanics reports original research in computational mechanics of enduring scholarly value. It focuses on areas that involve and enrich the rational application of mechanics, mathematics, and numerical methods in the practice of modern engineering. The journal investigates theoretical and computational methods and their rational applications. Areas covered include solid and structural mechanics, multi-body system dynamics, constitutive modeling, inelastic and finite deformation response, and structural control. The journal also covers fluid mechanics and fluid-structure interactions, biomechanics, free-surface and two-fluid flows, aerodynamics, fracture mechanics and structural integrity, multi-scale mechanics, particle and meshfree methods, transport phenomena, and heat transfer. Lastly, the journal publishes modern variational methods in mechanics in general.

CMFT is an international mathematics journal which publishes carefully selected original research papers in complex analysis (in a broad sense), and on applications or computational methods related to complex analysis. Survey articles of high standard and current interest can be considered for publication as well. Contributed papers should be written in English (exceptions in rare cases are tolerated), and in a lucid, expository style. Papers should not exceed 30 printed pages.

This journal publishes research on the analysis and development of computational algorithms and modeling technology for optimization. It examines algorithms either for general classes of optimization problems or for more specific applied problems, stochastic algorithms as well as deterministic algorithms. Computational Optimization and Applications covers a wide range of topics in optimization, including: large scale optimization, unconstrained optimization, constrained optimization, nondifferentiable optimization, combinatorial optimization, stochastic optimization, multiobjective optimization, and network optimization. It also covers linear programming, complexity theory, automatic differentiation, approximations and error analysis, parametric programming and sensitivity analysis, management science, and more. This peer-reviewed journal features both research and tutorial papers that provide theoretical analysis, along with carefully designed computational experiments.Officially cited as: Comput Optim Appl

Computational Psychiatry publishes original research articles and reviews that involve the application, analysis, or invention of theoretical, computational and statistical approaches to mental function and dysfunction. Topics include brain and behavioral modeling over multiple scales and levels of analysis, and the use of these models to understand psychiatric dysfunction, its remediation, its relation to social or biological factors, and the development and sustenance of healthy cognition throughout the lifespan.

Social networks, Social computing, Mathematics of social networks, Computational aspects of social networks, Therory of social computing

Computational Statistics (CompStat) is an international journal that fosters the publication of applications and methodological research in the field of computational statistics. The journal provides a forum for computer scientists, mathematicians, and statisticians working in a variety of areas in statistics, including biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge-based systems, and Bayesian computing. CompStat papers emphasize the contribution to and influence of computing on statistics and vice versa. The journal also publishes hardware, software, and package reports as well as book reviews.Officially cited as: Comput Stat

Computational Statistics & Data Analysis (CSDA), the official journal of the International Association of Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of three refereed sections, and a fourth section dedicated to news on statistical computing. The refereed sections are divided into the following subject areas:I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics, computational econometrics, computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article.II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, econometrics, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures.Statistical methodology includes, but not limited to: bootstrapping, classification techniques, clinical trials, data exploration, density estimation, design of experiments, pattern recognition/image analysis, parametric and nonparametric methods, statistical genetics, Bayesian modeling, outlier detection, robust procedures, cross-validation, functional data, fuzzy statistical analysis, mixture models, model selection and assessment, nonlinear models, partial least squares, latent variable models, structural equation models, supervised learning, signal extraction and filtering, time-series modelling, longitudinal analysis, multilevel analysis and quality control.III) Special Applications - Manuscripts at the interface of statistics and computing (e.g., comparison of statistical methodologies, computer-assisted instruction for statistics, simulation experiments). Advanced statistical analysis with real applications (economics, social sciences, marketing, psychometrics, chemometrics, signal processing, finance, medical statistics, environmentrics, statistical physics).