| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Engineering and Technology | 141 | 73 | 100 | 38 |
The journal covers a variety of subjects, including Finite element method, Artificial intelligence, Mathematical optimization, Applied mathematics and Algorithm. It addresses concerns in Finite element method which are intertwined with other disciplines, such as Discretization, Numerical analysis, Mathematical analysis and Mechanical engineering. Topics in Artificial intelligence explored in Archives of Computational Methods in Engineering were investigated in conjunction with research in Field (computer science), Machine learning, Computer vision and Pattern recognition.
The journal features Mathematical optimization research that overlaps with concepts in Convergence (routing). Applied mathematics and Nonlinear system are closely related fields of research discussed in the journal.
The published articles aim to foster the development of research in Finite element method, Mathematical optimization, Applied mathematics, Algorithm and Nonlinear system. The most cited papers explore issues in Finite element method which can be linked to other research areas like Element (category theory), Computation and Mathematical analysis. Issues in Nonlinear system were discussed in the published articles, taking into consideration concepts from other disciplines like Numerical analysis, Statistical physics and Model order reduction.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Archives of Computational Methods in Engineering (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Archives of Computational Methods in Engineering (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 8.04% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 17.11% were posted by at least one author from the top 10 institutions publishing in the journal. Another 12.17% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.29% of all publications and 49.43% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Antonio Maria D’Altri;Vasilis Sarhosis;Gabriele Milani;Jan Rots
(2020)Majdi Flah;Itzel Nunez;Wassim Ben Chaabene;Moncef L. Nehdi
(2021)Shuyuan Xu;Jun Wang;Wenchi Shou;Tuan Ngo
(2021)Unknown
(2022)Grace C Y Peng;Mark Alber;Adrian Buganza Tepole;William R Cannon
(2021)Francisco Chinesta;Elías G. Cueto;Emmanuelle Abisset-Chavanne;Jean Louis Duval
(2020)Ali Riza Yildiz;Hammoudi Abderazek;Seyedali Mirjalili
(2020)Ahad Javanmardi;Zainah Ibrahim;Khaled Ghaedi;Hamed Benisi Ghadim
(2020)Zeng Meng;Zeng Meng;Gang Li;Xuan Wang;Sadiq M. Sait
(2021)Masoud Abedini;Chunwei Zhang
(2021)For students interested in pursuing computer science in the USA, exploring online self paced college courses can offer flexibility and convenience, allowing learners to balance studies with work or other commitments. These programs are especially valuable for those who prefer a personalized learning pace without strict deadlines.
When considering advanced education, many opt for an affordable master degree online to deepen their expertise in key areas such as artificial intelligence, cybersecurity, or software development. Affordability combined with accreditation ensures both value and quality of education.
Starting with an associate degree is another practical pathway. Finding a community colleges near me can be an excellent way to enter the field through lower-cost, accessible programs that build foundational skills before transferring to a four-year university.
Lastly, verifying the institution’s credentials through online accredited colleges ensures that degrees are recognized by employers and meet industry standards. This accreditation is critical for long-term career success in the competitive tech landscape.
