Journal Information
Journal of Computational Science
http://www.journals.elsevier.com/journal-of-computational-science/
Impact Factor:
3.976
Publisher:
Elsevier
ISSN:
1877-7503
Viewed:
14428
Tracked:
9
Call For Papers
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.

The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.

This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.

Computational science typically unifies three distinct elements:

• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).

The Journal of Computational Science aims to be an international platform to exchange novel research results in simulation based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices.

The journal welcomes original, unpublished high quality contributions in the field of computational science at large, addressing one or more of the aforementioned elements.
Last updated by Dou Sun in 2022-01-29
Special Issues
Special Issue on Machine Learning applications in Complex Networks
Submission Date: 2022-08-15

Machine learning and complex network are currently at the threshold of being applied to important and exciting problems in complex systems within the field of physics, molecular biology, ecology, neuroscience, medicine, economics, social science, human behaviour, and climate science. It is the aim of this Special Issue to partake in this burgeoning combination of machine learning and complex networks in topics such as: network inference; link prediction; prediction of dynamical processes in networks; network sampling; patterns in networks; community detection; inference of network connections from time series; network classification; applications of machine learning for controlling dynamical processes; causal inference applications.
Last updated by Dou Sun in 2022-02-27
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