Preface
The 2024 International Conference on Applied Mathematics and Digital Simulation (AMDS 2024) was successfully held on December 30-31, 2024 in Wuhan, China. The conference proceedings comprise the contributions from participants, offering readers an extensive overview of methodologies, concepts, and tools in mathematical, statistical, and computational sciences. There are 152 participants in the AMDS 2024 conference. The program comprises four keynote addresses (each lasting 35-45 minutes), 85 oral presentations (each consisting of a 15-minute presentation followed by a 3-minute Q & A session), 33 e-posters, and 44 attendees who will be listening only. The oral presentations are divided into four sessions: Mathematics and Computational Theory, Mathematical Modelling and Machine Learning Methods, Mathematical Modelling in Engineering, and Fluid Dynamics and Mechanics. The primary objective of the conference is to emphasize the development of theories and applications while fostering interdisciplinary collaboration among mathematicians, statisticians, scientists, engineers, and researchers from related fields. This volume encompasses a selection of papers from the AMDS2024 conference. All accepted manuscripts underwent rigorous peer review by two to three reviewers in a double-blind process. A total of 39 reviewers contributed their expertise, providing constructive feedback to authors to ensure the high quality and integrity of the manuscripts. The volume is organized into seven thematic sections: Applied Mathematics and Computational Theory, Mathematical Modelling and Machine Learning Methods, Statistical Modelling and Data Analysis, Mathematical Modelling in Engineering, Fluid Dynamics and Mechanics, Control and Automation and Mathematical Modelling in Physics. These sections collectively present a comprehensive and current overview of principles, methodologies, and tools for mathematical simulation and modelling across diverse research fields. List of Conference Committees are available in this PDF.
Publication Details
- Journal
- Journal of Physics Conference Series
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1088/1742-6596/3004/1/011001
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00