Journals in Mathematics general
Journals in Mathematics general
- ISSN: 0001-8708
Advances in Mathematics
Emphasizing contributions that represent significant advances in all areas of pure mathematics, Advances in Mathematics provides research mathematicians with an effective medium for communicating important recent developments in their areas of specialization to colleagues and to scientists in related disciplines.Benefits to authors We 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. This journal has an Open Archive. All published items, including research articles, have unrestricted access and will remain permanently free to read and download 48 months after publication. All papers in the Archive are subject to Elsevier's user license.If you require any further information or help, please visit our Support Center.
- ISSN: 0020-7462
International Journal of Non-Linear Mechanics
The International Journal of Non-Linear Mechanics provides a specific medium for dissemination of high-quality research results in the various areas of theoretical, applied, and experimental mechanics of solids, fluids, structures, and systems where the phenomena are inherently non-linear.The journal brings together original results in elasticity, plasticity, dynamics, vibrations, wave-propagation, rheology, fluid-structure interaction systems, stability, biomechanics, micro- and nano-structures, materials, metamaterials, and in other diverse areas.Papers may be analytical, computational or experimental in nature. Treatments of non-linear differential equations wherein solutions and properties of solutions are emphasized but physical aspects are not adequately relevant, will not be considered for possible publication. Both deterministic and stochastic approaches are fostered. Contributions pertaining to both established and emerging fields are encouraged.
- ISSN: 0167-6377
Operations Research Letters
Operations Research Letters (ORL) is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. ORL welcomes pure methodological papers and applied papers with firm methodological grounding. All articles are restricted to at most eight journal pages, with the option to relegate proofs and additional material to an online appendix. The main criteria for the papers to be published are quality, originality, relevance, and clarity. The journal's traditional strength is in methodology, including theory, modelling, algorithms, and computational studies. Please find below a full description of the areas covered by the journal.Area EditorsApproximation Algorithms for Combinatorial Optimization Problems Area Editor: Leah Epstein Associate Editors: M. Chrobak, K. Elbassioni, M. Feldman, J. Hurink, N. Olver, J. Sgall, J. Verschae The area covers all issues relevant to the development of efficient approximate solutions to computationally difficult problems. This includes worst case analysis or competitive analysis of approximation algorithms, and complexity results.Submissions can be articles consisting of theoretical work in the area, or articles combining significant theoretical contributions of mathematical flavor with computational investigations of heuristic approaches. Articles in the area of discrete optimization that do not belong to the scope of other areas may be submitted to this area as well.Computational Social Science Area Editor: Vianney Perchet Associate Editors: A. Drutsa, P. Mertikopoulos, R. Smorodinsky This area publishes papers focusing on data-driven procedures, either from a theoretical or an applied perspective, in operation research, games, economics and other social science. The scope includes: sample/computational complexity of mechanisms, learning in games/OR/social science, empirical solutions with AI algorithms (such as, but not limited to, deep learning techniques) of complex problems, etc. Continuous Optimization Area Editor: Hector Ramirez Associate Editors: M.F. Anjos, L.M. Briceno, D. Dadush, G. Eichfelder, D. Jiang, D. Orban, F. Schoen Papers in all fields of continuous optimization that are relevant to operations research are welcome. These areas include, but are not restricted to, linear programming, nonlinear programming (constrained or unconstrained, convex or nonconvex, smooth or nonsmooth, finite or infinite-dimensional... complementarity problems, variational inequalities, bilevel programming, and mathematical programs with equilibrium constraints. Financial Engineering Area Editor: Ning Cai Associate Editors: X. He, D. Mitchell Financial engineering utilizes methodologies of optimization, simulation, decision analysis and stochastic control to analyse the effectiveness and efficiency of financial markets. This area is interested in papers that innovate in terms of methods or that develop new models which guide financial practices. Examples include but are not limited to Fintech, financial networks, market microstructure, derivative pricing and hedging, credit and systemic risk, energy markets, portfolio selection. Game Theory Area Editor: Tristan Tomala Associate Editors: S. Beal, V. Ihele, D.W.K. Yeung, G. Zaccour This area publishes papers which use game theory to analyze operations research models or make theoretical contributions to the theory of games. The scope includes (but is not limited to): cooperative and non-cooperative games, dynamic games, mechanism and market design, algorithmic game theory, games on networks, games of incomplete information. Graphs & Networks Area Editor: Gianpaolo Oriolo Associate Editors: F. Bonomo, Y. Faenza, Z. Friggstad, L. Sanita The area seeks papers that apply, in original and insightful ways, discrete mathematics to advance the theory and practice of operations research, as well as those reporting theoretical or algorithmic advances for the area. Of particular, but not exclusive, interest are papers devoted to novel applications, telecommunications and transportation networks, graphs and web models and algorithms. Inventory and Supply Chain Optimization Area Editor: Sean Zhou Associate Editors: H. Abouee Mehrizi, A. Burnetas, X. Gong, Q. Li, J. Yang The area welcomes innovative papers focused on inventory control and supply management. Examples of topics include, but are not limited to, optimal sourcing, inventory and assortment selection, pricing and inventory optimization, capacity planning, multi-item/echelon systems, algorithms and bounds, near-optimal or asymptotic optimal solutions, and incentive design. Mixed Integer Optimization Area Editor: Marc Pfetsch Associate Editors: R. Fukasawa, L. Liberti, J.P. Vielma, G. Zambelli All submissions advancing the theory and practice of mixed integer (linear or nonlinear) programming like novel techniques and algorithmic approaches in convex relaxations, branch and cut, polyhedral combinatorics and theory driven heuristics are welcome. Case studies may be considered if they contribute to the general methodology. Operations Management Area Editor: Mahesh Nagarajan Associate Editors: L. Chu, Y. Ding, N. Golrezaei, T. Huh, R. Roet-Green, D. Saban, C. Shi, L. Zhu The OM department aims to publish short, focused high quality research in the area of operations management, broadly the field of operations research applied to management problems. We welcome papers that use a wide variety of methodologies, both descriptive as well as prescriptive in nature including optimization, applied probability, simulation, and game theory. Scheduling Area Editor: Marc Uetz Associate Editors: B. Moseley, E. Pesch, R. Van Stee We seek original and significant contributions to the analysis and solution of sequencing and scheduling problems. This includes structural and algorithmic results, in particular optimization, approximation and online algorithms, as well as game theoretic modeling. All results are welcome as long as the relevance of a problem and significance of the contribution is made compellingly clear. Stochastic Models and Data Science Area Editor: Henry LamAssociate Editors: H. Bastani, J. Dong, K. Murthy, I. Ryzhov, Y. Zhou The area seeks papers broadly on the interplay between operations research and machine learning and statistics where stochastic variability and uncertainty play a crucial role. The area values both papers that develop or utilize stochastic analysis and computation in data science problems, including but not limited to reinforcement learning, stochastic iterative algorithms for model estimation or training, probabilistic analysis of statistical and machine learning tools, sampling and Monte Carlo methods, and also papers that integrate learning or statistical techniques into stochastic modeling to enhance prediction or decision-making for a wide variety of systems. Stochastic Networks and Queues Area Editor: Harsha HonnappaAssociate Editors: E. Ozkan, W. Wang, Y. ZhaoThe area seeks papers that contribute to the modeling, analysis or innovative application of stochastic networks or queues. Work submitted should propose original models and develop novel analytical or computational methods more than incremental extensions. Examples of relevant application areas include but are not limited to supply chain management, manufacturing, financial engineering, healthcare, revenue management, service operations, telecommunications, sharing economy, online markets and public sector operations research. Application-oriented papers should demonstrate direct practical impact and have a strong methodological component as well.Stochastic Optimization and Machine Learning Area Editor: Angelos Georghiou Associate Editors: M. Bodur, M. Claus, E. Feinberg, P. Vayanos The Stochastic Optimization and Machine Learning area of Operations Research Letters solicits original articles that generate novel insights into problems that arise in optimization under uncertainty and in machine learning. The focus is broad and encompasses, among others, stochastic (dynamic) programming, (distributionally) robust optimization, data-driven optimization as well as the interface of machine learning with traditional areas of operations research. Successful submissions in this area are expected to make a clear and meaningful academic contribution, which may be through the study of new problems, models, solution techniques, performance analysis and convincing and reproducible numerical evaluations.
- ISSN: 0377-2217
European Journal of Operational Research
Published in collaboration with the Association of European Operational Research Societies (EURO)The European Journal of Operational Research (EJOR) publishes high quality, original papers that contribute to the methodology of operational research (OR) and to the practice of decision making. EJOR contains the following types of papers:• Invited Reviews, explaining to the general OR audience the developments in an OR topic over the recent years • Innovative Applications of OR, describing novel ways to solve real problems • Theory and Methodology Papers, presenting original research results contributing to the methodology of OR and to its theoretical foundations, • Short Communications, if they correct important errors found in papers previously published in EJORThe Theory and Methodology Papers are classified into one of the seven headings:• Continuous Optimization • Discrete Optimization • Production, Manufacturing and Logistics • Stochastics and Statistics • Decision Support • Computational Intelligence and Information Management • Interfaces with Other DisciplinesBenefits to authors We 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 Center
- ISSN: 0167-2789
Physica D: Nonlinear Phenomena
Physica D (Nonlinear Phenomena) publishes research and review articles reporting on theoretical and experimental work, techniques, and ideas that advance the understanding of nonlinear phenomena. The scope of the journal encompasses mathematical methods for nonlinear systems including: wave motion, pattern formation and collective phenomena in physical, chemical and biological systems; hydrodynamics and turbulence; integrable and Hamiltonian systems; and data-driven dynamical systems. The journal encourages submissions in established and emerging application domains, for example applications of nonlinear science to artificial intelligence, robotics, control theory, complex networks, and social and economic dynamics.
- ISSN: 1063-5203
Applied and Computational Harmonic Analysis
Applied and Computational Harmonic Analysis (ACHA) is an interdisciplinary journal that publishes high-quality papers in all areas of mathematical sciences related to the applied and computational aspects of harmonic analysis, with special emphasis on innovative theoretical development, methods, and algorithms, for information processing, manipulation, understanding, and so forth. The objectives of the journal are to chronicle the important publications in the rapidly growing field of data representation and analysis, to stimulate research in relevant interdisciplinary areas, and to provide a common link among mathematical, physical, and life scientists, as well as engineers. Applied and computational harmonic analysis covers, in the broadest sense, topics that include but not limited to:I Signal and Function Representations • continuous and discrete wavelet transform • wavelet frames • wavelet algorithms •local time-frequency and time-scale basis functions • multi-scale and multi-level methods • refinable functionsII Representation of Abstract and High-dimensional Objects • diffusion wavelets and geometry • harmonic analysis on graphs and trees • sparse data representation • compressive sampling • compressed sensing • matrix completion • random matrices and projections • data dimensionality reduction • high-dimensional integrationIII Application Areas • data compression • signal and image processing • learning theory and algorithms • computer-aided geometric design • extra large data analysis and understanding • data recovery and image inpainting • data mining • hyperspectral imaging • novel sensors and systemsThis journal has an Open Archive. All published items, including research articles, have unrestricted access and will remain permanently free to read and download 48 months after publication. All papers in the Archive are subject to Elsevier's user license.
- ISSN: 0307-904X
Applied Mathematical Modelling
Applied Mathematical Modelling focuses on significant and novel scientific developments for mathematical modelling and computational methods and tools for engineering, industrial and environmental systems and processes leading to future innovations and novel technologies.The topics considered are: heat transfer, fluid mechanics, computational fluid dynamics and electromagnetics, and transport phenomena; solid mechanics and mechanics of metals; electromagnets and magnetohydrodynamics... reliability modelling and system optimization; modelling of inventory, industrial, manufacturing and logistics systems with managerial insights; engineering systems and structures; mineral and energy resources; software engineering developments; digital twins; materials; unmanned vehicles; robotics; network traffic control; energy sustainability models; optimization; population dynamics with realistic scenarios; high-performance methods for data-driven engineering applications; numerical procedures; computational intelligence in complex engineering problems.Applied Mathematical Modelling is primarily interested in: Papers developing increased insights into real-world problems through novel analytical or semi-analytical mathematical and computational modelling.Papers with multi- and interdisciplinary topics, including linking with data driven models and applications.Papers on novel applications or a combination with the above.Papers employing existing methods must demonstrate significant novelty in the solution of practical problems. Model validation, verification and reproducibility is a fundamental principle for published papers.Papers based on fuzzy logic in decision-making, financial mathematics, heuristic algorithms, neural networks, data modelling, game-theoretical, fractional differential equations, bifurcation and numerical methods papers are not considered unless they solve practical problems, supported by reasonable empirical evidence. Submissions with no real-world application will not be considered.This journal has an Open Archive. All published items, including research articles, have unrestricted access and will remain permanently free to read and download 48 months after publication. All papers in the Archive are subject to Elsevier's user license.If you require any further information or help, please visit our Support Center
- ISSN: 0893-6080
Neural Networks
Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks, including deep learning and related approaches to artificial intelligence and machine learning. Neural Networks welcomes submissions that contribute to the full range of neural networks research, from cognitive modeling and computational neuroscience, through deep learning algorithms and mathematical analyses, to engineering and technological applications of systems that significantly use neural network concepts and learning techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspire... artificial intelligence. Accordingly, the Neural Networks editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. On the other hand, neural networks should be central to submissions. The journal publishes articles, letters, and reviews/tutorials, as well as letters to the editor, editorials, and current events. Articles are published in one of five sections: learning systems, cognitive science, neuroscience, mathematical and computational analysis, engineering and applications.Neural Networks is the archival journal of three of the oldest and most prominent neural network societies: the International Neural Network Society (INNS), the Asia-Pacific Neural Network Society (APNNS), and the Japanese Neural Network Society (JNNS). A subscription to the journal is included with membership in each of these societies.Members of the societies listed as affiliated to the journal receive a 25% discount on Article Processing Charges when they publish an article in Neural Networks as an author - this will be detailed through the submission and production processes.INNS members receive additional support with a further 25% contribution from the INNS towards their open access article processing charges for accepted papers. If you are an INNS member, please submit this web form to receive reimbursement for 25% of your open access article processing charges. You will be asked to submit a PDF copy of your acceptance and production email, as well as a Wire Transfer Form.
- ISSN: 0723-0869
Expositiones Mathematicae
This journal publishes articles in English, French or German in all branches of mathematics under the headings “Survey Articles”, "Main Research Articles" and "Short Research Notes". Survey articles - are expositions on contemporary mathematical research written in a way that a research student or a mathematician who may not be an expert on the topic can read them profitably. There is no page limit for survey articles.Main research articles - must contain significant new results, provide enough background information on the research topic and make high level research accessible to a broad audience. Main research articles are expected to have at least fifteen pages. Short research notes - can be slightly higher-level research on a specialized topic and are expected to contain ten or fewer pages. Clarity of exposition, accuracy of the details, quality of research results, and the relevance and interest of the subject matter will be the decisive factors in our acceptance for publication of an article.
- ISSN: 0047-259X
Journal of Multivariate Analysis
The Journal of Multivariate Analysis (JMVA) is the central venue for the publication of new, relevant methodology and particularly theoretical developments of multivariate statistics combined with innovative applications pertaining to the analysis and interpretation of multidimensional data. Papers making substantial contributions to regression or time series analysis for multidimensional response variables are invited.Please note that a JMVA article is of length 15-25 pages. All proofs of propositions, theorems and lemmas should appear in the main body of the text or an appendix (not in a supplement).The journal welcomes contributions to all aspects of multivariate data analysis and modeling, including:cluster analysis, discriminant analysis, factor analysis, independent component analysis and multidimensional continuous or discrete distribution theory, where the theory should connect to multivariate statistical inference.Topics of current interest include, but are not limited to, inferential aspects of: Change point analysisCopula modelingFunctional data analysisGraphical modelingHigh-dimensi... data analysisImage analysisMultivariate extreme-value theoryMultivariate mixed modelsSparse modelingSpatial statistics,Submissio... dealing with univariate models, including regression models with a single response variable and univariate time series models, are usually deemed to fall outside the journal's remit.Papers whose content is probabilistic in nature or whose main contribution is to substantive areas (e.g., actuarial science, biostatistics, economics, finance or hydrology) typically fall outside the journal's scope and will only be considered for publication if the statistical methodology used is both novel and broadly applicable.Finally, note that contributors to multivariate survival analysis, reliability theory and statistical quality control should submit their papers to journals specializing in these areas to ensure that their work reaches the targeted audience.This journal has an Open Archive. All published items, including research articles, have unrestricted access and will remain permanently free to read and download 48 months after publication. All papers in the Archive are subject to Elsevier's user license.