Journal information

  • Chi Wang Shu
Publishing model
Hybrid (Transformative Journal). Learn about publishing Open Access with us

Journal metrics

2.592 (2020)
Impact factor
2.744 (2020)
Five year impact factor
101 days
Submission to first decision
263 days
Submission to acceptance
184,209 (2020)

Latest articles

Journal updates

  • Topical Collection: Beyond traditional AI: the impact of Machine Learning on Scientific Computing

    Guest Editors
    Francesco Piccialli,
    Salvatore Cuomo,
    Boumediene Hamzi,
    Jan Hesthaven,

    Submission deadline: December 31, 2021

    We are pleased to solicit submissions to the Topical Collection "Beyond traditional AI: the impact of Machine Learning on Scientific Computing".
    In order to add another piece to a complicated but fascinating puzzle, this topical collection aims to attract high-quality contributions to investigate both the role of ML/DL methodologies in applied mathematics and how Scientific Computing can benefit from learning paradigms.

  • Topical Collection dedicated to the ICERM Spring 2020 semester program on Model Order Reduction

    Guest Editors: Yanlai Chen,; Sigal Gottlieb,; Serkan Gugercin,; Misha Kilmer,; Akil Narayan,; and Daniele Venturi,

    Submission deadline: December 1, 2020

    We are pleased to solicit submissions to the Topical Collection of JSC dedicated to the ICERM Spring 2020 program on "Model Order Reduction. Aiming to focus research effort on current areas of promising research and to galvanize new and existing collaborations, the Spring 2020 ICERM semester program focused on both theoretical investigation and practical algorithm development for reduction in the complexity - the dimension, the degrees of freedom, the data - arising in these models. The program in particular aimed to integrate diverse fields of mathematical analysis, statistical sciences, data and computer science, and specifically to attract researchers working in the areas of model order reduction, data-driven model calibration and simplification, computational approximation in high dimensions, and data-intensive uncertainty quantification. The four broad thrusts of the program are (1) mathematics of reduced order models, (2) algorithms for approximation and complexity reduction, (3) computational statistics and data-driven techniques, and (4) application-specific design.

  • COVID-19 and impact on peer review

    As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times.  Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time.

View all updates


Note this is only the net price. Taxes will be calculated during checkout.
  • Immediate online access with complete access to all articles starting 1997
  • Downloadable in PDF format
  • Subscription expires 12/31/2021

About this journal

Electronic ISSN
Print ISSN
Abstracted and indexed in
  1. ACM Digital Library
  2. BFI List
  3. CNKI
  4. DBLP
  5. Dimensions
  6. EBSCO Applied Science & Technology Source
  7. EBSCO Computer Science Index
  8. EBSCO Computers & Applied Sciences Complete
  9. EBSCO Discovery Service
  10. EBSCO Engineering Source
  11. EBSCO STM Source
  12. EI Compendex
  13. Google Scholar
  14. INSPEC
  15. Japanese Science and Technology Agency (JST)
  16. Journal Citation Reports/Science Edition
  17. Mathematical Reviews
  18. Naver
  19. OCLC WorldCat Discovery Service
  20. ProQuest Advanced Technologies & Aerospace Database
  21. ProQuest Central
  22. ProQuest SciTech Premium Collection
  23. ProQuest Technology Collection
  24. ProQuest-ExLibris Primo
  25. ProQuest-ExLibris Summon
  26. SCImago
  27. SCOPUS
  28. Science Citation Index Expanded (SciSearch)
  29. TD Net Discovery Service
  30. UGC-CARE List (India)
  31. zbMATH
Copyright information

Rights and permissions

Springer policies

© Springer Science+Business Media, LLC, part of Springer Nature