COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS纯数学与应用数学通讯

COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS(英文缩写 COMMUN PUR APPL MATH),ISSN 0010-3640,eISSN 1097-0312,中文译名:纯数学与应用数学通讯 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。

2026 年数据 · 影响因子
3.500
JCR 分区
Q1
CAS 分区
B1
近一年发文量
0
本站 PubMed 收录统计

发文量统计区间:2025-11-01 至 2026-10-31,按本站收录文献的发表日期统计。

ISSN: 0010-3640 · eISSN: 1097-0312 · 缩写: COMMUN PUR APPL MATH ·中文: 纯数学与应用数学通讯

期刊介绍

选择期刊介绍栏目

期刊简介

《Communications on Pure and Applied Mathematics》是数学领域历史悠久且享有盛誉的综合性期刊,由柯朗研究所主办。它发表纯数学与应用数学的高质量研究论文,涵盖分析、偏微分方程、几何、概率、数学物理及数值分析等方向。读者主要为数学专业研究人员、研究生以及从事相关交叉学科工作的科学家,强调理论深度与创新性,在数学界具有广泛影响力。

研究方向

期刊覆盖纯数学与应用数学的广泛主题,包括偏微分方程、微分几何、拓扑、代数、概率论、数学物理、数值分析与计算数学等。论文类型以长篇原创研究论文为主,也偶尔发表重要综述或具有突破性的短篇工作,要求对数学理论或应用有实质性贡献。

期刊特色

研究取向偏重理论严谨性与问题重要性,论文通常篇幅较长、技术性强,鼓励跨分支的方法融合。适合已具备扎实数学基础、希望在高水平综合数学期刊展示系统性成果的研究人员,尤其适合从事分析、几何与数学物理方向的研究者。

投稿难度

投稿难度较高,期刊对原创性、技术深度和完整性有严格要求,竞争激烈。建议在投稿前确保结果具有显著推进意义,写作清晰且证明完整,并参考近期发表论文的风格与标准,做好多轮修改和较长时间审稿的准备。

历年影响因子趋势

JCR 数据年份影响因子JCR 分区
20212.774Q1
20223.000Q1
20233.100Q1
20242.700Q1
20253.500Q1

COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS 最新收录文献

  1. JCR分区: Q1 CAS分区: B1 影响因子: 3.5

    1. Electrophysiology.

    作者:
    Boyce E Griffith, Charles S Peskin
    日期:
    2013-10-09

    Electrical signaling is a fast mode of communication for cells within an organism. We are concerned here with the formulation and analysis of mathematical models that are used to describe this important class of physiological processes. These models generally take the form of partial differential equations that are descendants of those introduced by Hodgkin and Huxley to describe the propagation of an action potential along the squid giant axon. We review that work here and then go on to describe more recent variations on the Hodgkin-Huxley theme, including the three-dimensional bidomain (and monodomain) equations for cardiac electrophysiology, multiscale models for the heart that take cellular structure into account near the action potential wavefront, and finally a more detailed reformulation of electrophysiology in terms of electrodiffusion.

  2. JCR分区: Q1 CAS分区: B1 影响因子: 3.5

    2. Nonlinear Dynamics of Neuronal Excitability, Oscillations, and Coincidence Detection.

    作者:
    John Rinzel, Gemma Huguet
    日期:
    2013-09

    We review some widely studied models and firing dynamics for neuronal systems, both at the single cell and network level, and dynamical systems techniques to study them. In particular, we focus on two topics in mathematical neuroscience that have attracted the attention of mathematicians for decades: single-cell excitability and bursting. We review the mathematical framework for three types of excitability and onset of repetitive firing behavior in single-neuron models and their relation with Hodgkin's classification in 1948 of repetitive firing properties. We discuss the mathematical dissection of bursting oscillations using fast/slow analysis and demonstrate the approach using single-cell and mean-field network models. Finally, we illustrate the properties of Type III excitability in which case repetitive firing for constant or slow inputs is absent. Rather, firing is in response only to rapid enough changes in the stimulus. Our case study involves neuronal computations for sound localization for which neurons in the auditory brain stem perform extraordinarily precise coincidence detection with submillisecond temporal resolution.

  3. JCR分区: Q1 CAS分区: B1 影响因子: 3.5

    3. Vector Diffusion Maps and the Connection Laplacian.

    作者:
    A Singer, H-T Wu
    日期:
    2012-08

    We introduce (VDM), a new mathematical framework for organizing and analyzing massive high-dimensional data sets, images, and shapes. VDM is a mathematical and algorithmic generalization of diffusion maps and other nonlinear dimensionality reduction methods, such as LLE, ISOMAP, and Laplacian eigenmaps. While existing methods are either directly or indirectly related to the heat kernel for functions over the data, VDM is based on the heat kernel for vector fields. VDM provides tools for organizing complex data sets, embedding them in a low-dimensional space, and interpolating and regressing vector fields over the data. In particular, it equips the data with a metric, which we refer to as the . In the manifold learning setup, where the data set is distributed on a low-dimensional manifold ℳ embedded in ℝ , we prove the relation between VDM and the connection Laplacian operator for vector fields over the manifold.

在 COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS 中搜索更多文献

支持中英文检索 · 智能翻译 · 影响因子 · PDF 下载 · AI 文献阅读

指标接近的期刊