Advances in Continuous and Discrete Models

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, including stability and optimal control. Mathematical Biology and Bioengineering: Welcoming innovative simple mathematical modeling efforts inspired by biological phenomena, including but not limited to ecology and epidemiology. We also welcome the analysis of mathematical properties of existing models with biological implications through new technologies.

摘自期刊官网 Aims & Scope

研究方向（LetPub）：Multiple

中科院期刊分区表

数据库收录

SCIEScopusLetPub

国际期刊预警名单：各版本均未列入LetPub

不同版本、大类与小类分别展示，不将不同评价口径合并为一个分区值；投稿与考核前请确认所在机构采用的版本。

影响力指标

影响因子

1.66LetPub · 2026-09-13

影响因子（出版商公布） 2025

1.6出版商官网 · 2026-09-15

五年影响因子（出版商公布） 2025

1.9出版商官网 · 2026-09-15

CiteScore 2026-06

2.9LetPub · 2026-09-13

SJR 2026-06

0.42LetPub · 2026-09-13

SNIP 2026-06

0.78LetPub · 2026-09-13

h-index

16OpenAlex · 2026-09-13

i10-index

41OpenAlex · 2026-09-13

两年平均被引

1.85OpenAlex · 2026-09-13

自引率

6.2%LetPub · 2026-09-13

Gold OA 文章占比

99.3%LetPub · 2026-09-13

发文规模

年文章数

179LetPub · 2026-09-13

年下载量（出版商公布） 2025

2,000,000出版商官网 · 2026-09-15

不同来源的统计口径与观测时间不同，页面保留各来源原值；“出版商公布”为期刊官网自述数据。

投稿到首次决定

24 天出版商官网 · 2026-09-15

社区反馈来自投稿者样本，置信度 0.5，不代表期刊承诺；“出版商公布”的时间节点定义以官网为准。

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摘录来自期刊官网页面，仅保留原文片段；“模型摘要”由 Workers AI 生成并经原文校验，以官网为准。

出版商

Springer Nature

ISSN

2731-4235

官方链接

• 期刊主页 ↗OpenAlex
• 专刊 / 特刊征稿 ↗出版商官网