Manuscript Topics

In this special issue, we are interested in accurate and efficient numerical methods for multi-component phase-field models and vector-valued partial differential equations, which find extensive applications in various scientific domains. Our primary objective is to present cutting-edge research that introduces novel numerical techniques specifically tailored for multi-component phase-field models and vector-valued partial differential equations.

To enhance the practical utility of the published papers, we strongly encourage authors to include the source codes in the Appendix section. These source codes should be meticulously documented, ensuring they are user-friendly and comprehensible even to researchers who may be new to the field. The intention is to provide a valuable resource that empowers potential researchers to effortlessly follow and execute the codes, enabling them to reproduce the computational results presented in the papers.

By sharing the source codes alongside the research articles, we aim to foster an environment of transparency and reproducibility in computational scientific research. This approach not only facilitates the dissemination of knowledge but also allows researchers to build upon the published work more effectively, using the new numerical methods in their own investigations. It encourages collaboration and inspires further advancements in the field of multi-component phase-field models and vector-valued partial differential equations.

This special issue in Accurate and efficient numerical methods for the multi-component partial differential equations including the phase-field models copes with (but is not limited to) the subjects as follows:
• Multi-component phase-field models
• Multi-component image segmentation
• Multi-component Navier-Stokes equations
• Multi-component volume reconstruction from two-dimensional slice data
• Multi-component data classification

Instruction for Authors
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Please submit your manuscript to online submission system
https://aimspress.jams.pub/