SIAM Journal on Scientific Computing

Aims & Scope

The purpose of SIAM Journal on Scientific Computing (SISC) is to advance computational methods for solving scientific and engineering problems.

SISC papers are classified into three categories: 1.

Methods and Algorithms for Scientific Computing: Papers in this category may include theoretical analysis, provided that the relevance to applications in science and engineering is demonstrated.

They should contain meaningful computational results and theoretical results or strong heuristics supporting the performance of new algorithms. 2.

Computational Methods in Science and Engineering: Papers in this section will typically describe novel methodologies for solving a specific problem in computational science or engineering.

They should contain enough information about the application to orient other computational scientists but should omit details of interest mainly to the applications specialist. 3.

Software and High-Performance Computing: Papers in this category should concern the novel design and development of computational methods and high-quality software, parallel algorithms, high-performance computing issues, new architectures, data analysis, or visualization.

The primary focus should be on computational methods that have potentially large impact for an important class of scientific or engineering problems.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.6
2024	3.00

SJR (SCImago Journal Rank)

Year	Value
2024	1.634

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	169

Journal Rank

Year	Value
2024	2068

Journal Citation Indicator

Year	Value
2024	2359

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Atomic Decomposition by Basis Pursuit

  Citation: 4599

  Authors: Scott Shaobing, David L., Michael A.

• A Limited Memory Algorithm for Bound Constrained Optimization

  Citation: 4539

  Authors: Richard H., Peihuang, Jorge, Ciyou

• The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

  Citation: 3834

  Authors: Dongbin, George Em

• A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs

  Citation: 3749

  Authors: George, Vipin

• The MATLAB ODE Suite

  Citation: 2849

  Authors: Lawrence F., Mark W.

• The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems

  Citation: 2490

  Authors: Per Christian, Dianne Prost

• Tensor-Train Decomposition

  Citation: 1895

  Authors: I. V.

• Nonlinear Model Reduction via Discrete Empirical Interpolation

  Citation: 1466

  Authors: Saifon, Danny C.

• Probing the Pareto Frontier for Basis Pursuit Solutions

  Citation: 1331

  Authors: Ewout, Michael P.

• High-Order Collocation Methods for Differential Equations with Random Inputs

  Citation: 1191

  Authors: Dongbin, Jan S.

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