Aims & Scope

Computers & Fluids is multidisciplinary. The term 'fluid' is interpreted in the broadest sense. Hydro- and aerodynamics, high-speed and physical gas dynamics, turbulence and flow stability, multiphase flow, rheology, tribology, aeroacoustics and fluid-structure interaction are all of interest, provided that computer technique plays a significant role in the associated studies or design methodology.

Applications will be found in most branches of engineering and science: mechanical, civil, chemical, aeronautical, medical, geophysical, nuclear and oceanographic. These will involve problems of air, sea and land vehicle motion and flow physics, energy conversion and power, chemical reactors and transport processes, ocean and atmospheric effects and pollution, biomedicine, noise and acoustics, and magnetohydrodynamics amongst others.

The development of numerical methods relevant to fluid flow computations, computational analysis of flow physics and fluid interactions and novel applications to flow systems and to design are pertinent to Computers & Fluids.

The journal also accepts papers dealing with uncertainty quantification in fluid flow simulations, reduced-order and surrogate models for fluid flows, optimization and control.

Papers dealing with machine learning approaches applied to fluid flow modeling are welcome, provided they show excellent scientific character. In particular, the authors are encouraged to perform comparisons with traditional numerical reconstruction methods, to provide a clear presentation of training vs validation cases, together with sufficient diversity in these cases, to analyze the physical consistency/theoretical analysis of the ML model, and to discuss the limitations of the method as well as its merits.

Special Issue on Proceedings of the AI and Fluid Mechanics Symposium
提出日: 2026-03-30

Fluid mechanics has been explored by experimental, theoretical and traditional computational methods. Recently, there has been a resurgence of data-driven and machine learning methods to provide improved understanding and control of fluid flows. At the same time, novel approaches of solving the fluid flow conservation equations based on AI techniques are under development. As AI continues to reshape traditional modelling approaches, this special volume articulates ongoing cutting-edge works in the field covering both fundamentals and a wide range of industrial applications.

Guest editors:

Professor Emmanouil (Manolis) Gavaises
City St George University London, UK
m.gavaises@city.ac.uk 

Associate Prof Miguel Alfonso Mendez
von Karman Institute for Fluid Dynamics, Belgium
miguel.alfonso.mendez@vki.ac.be 

Professor George Karniadakis
Brown University, USA
george_karniadakis@brown.edu 

Special issue information:

Fluid mechanis and AI, machine learning, data-driven methods for analysis, modeling and control of fluid flows, including but not limited to:

- AI assisted turbulence modeling
- data assimilation and uncertainty quantification
- AI assisted surrogate models
- Generative AI in Fluid Mechanics
- Reinforcement Learning and its hybrid variants
- Physics Informed Machine Learning

Manuscript submission information:

Contributions to this Special Issue will be by invitation only.

The deadline for manuscript submission is 30/03/2026. 
Please ensure you read the Guide for authors - Computers & Fluids - ISSN 0045-7930 | ScienceDirect.com by Elsevier before writing your manuscript. Use this link to submit the manuscript: Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on Computers & Fluids | Journal | ScienceDirect.com by Elsevier . When submitting your manuscript please select the article type “VSI: AIFLUIDS”. 
For any inquiries about the appropriateness of contribution topics, please contact Professor Emmanouil (Manolis) Gavaises via m.gavaises@city.ac.uk.

Keywords:

AI assisted turbulence modeling
data assimilation and uncertainty quantification
AI assisted surrogate models
Generative AI in Fluid Mechanics
Reinforcement Learning and its hybrid variants
Physics Informed Machine Learning