Recent Developments in Mathematical Fluid Dynamics
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Physics".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 264
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue of Axioms aims to present and promote recent advances in the mathematical foundations and modern analytical, numerical, and computational methods for the study of fluid dynamics. As fluid flows play a central role in many physical, biological, and industrial processes, the rigorous understanding and modeling of their behavior remain a cornerstone of applied mathematics.
The focus of this Special Issue is on the development of axiomatic frameworks, new theoretical insights, and advanced numerical techniques that involve the mathematical modeling of fluids, whether incompressible, compressible, multiphase, non-Newtonian, or turbulent. Topics of interest include, but are not limited to, the following: the analysis of partial differential equations governing fluid motion; existence and uniqueness results; stability and control of flow systems; numerical schemes for multi-physics and multiscale phenomena; and applications to real-world systems.
This Special Issue also encourages contributions that bridge mathematical fluid dynamics with areas such as optimization, uncertainty quantification, machine learning, and high-performance computing. By assembling contributions from leading researchers and emerging scholars, this Special Issue will offer a snapshot of current trends and challenges, while highlighting open questions and directions for future work.
We look forward to your valuable contributions.
Dr. Andrea Chierici
Guest Editor
Manuscript Submission Information
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Keywords
- mathematical fluid dynamics
- partial differential equations
- multiphase flows
- non-Newtonian fluids
- numerical methods
- axiomatic modeling
- stability and control in fluid dynamics
- multiscale simulations
- high-performance computing
- machine learning in fluid dynamics
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