Mathematics

AAP Advances in Materials, Manufacturing and Computational Intelligence Techniques

Artificial Intelligence and Computational Methods for Multiphysics Modeling
Theory, Numerical Techniques, and Applications

Editors: Prof. (Dr.) Sahin Ahmed
Dr. Silpi Hazarika
Dr. Dhananjay Yadav
Dr. Mohammad Hassan

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Artificial Intelligence and Computational Methods for Multiphysics Modeling

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Series: AAP Advances in Materials, Manufacturing and Computational Intelligence Techniques

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Short description about the volume:


This book, Artificial Intelligence for Multiphysics Systems: Numerical Modelling and Applications, will provide a collective treatment of numerical methods and data-based approaches across multiple physical processes. It will provide a balanced treatment of multiphysics modeling by covering governing equations, numerical methods, and data-based approaches in an integrated manner. It also will include both engineering and biomedical applications, rather than focusing on a single domain. By combining fundamental concepts, computational methods, and practical case studies, the book aims to offer a more complete and organized resource for readers working on multiphysics systems through mathematical formulations.

This book will basically encapsulate various numerical methods and data-driven techniques for boundary value problems. The solvers, such as finite difference methods, finite element methods are to be incorporated to obtain solutions for suitable and acceptable findings. On the other hand, when the flow models are considered to be complicated or highly non-linear, we may utilize different data-driven tools such as ANN, SRM, and surrogate models to achieve better approximated outcomes.

One of the main aims of this book is to offer a presentation of theoretical aspects, simulation techniques, and feasible implications of the mathematical modelling in different aspects via AI. The chapters in all the segments are expected to describe the model, mathematical formulations of the stipulated problem, analysis of numerical methods, and model validation with better results. The physical meaning of the problem is carefully maintained, particularly when integrating data-driven approaches with conventional modelling methodologies.

The book will cover methods for creating and resolving transdisciplinary systems utilizing data-based AI and numerical techniques. Providing a clear grasp of the development, solution, and interpretation of mathematical models is the aim. Among the topics covered will be heat transfer, fluid movement, magnetohydrodynamic systems, nanofluids, and biological transport mechanisms. Methods for parameter estimation, uncertainty analysis, and model simplification will also be covered in the book. Useful examples will be given to show how these methods are applied to real-world problems.

Applications in fluid mechanics, heat and mass transport, magnetohydrodynamic flows, nanofluids, and bio-convective systems will also be covered in this book. Additionally, it will include a few biomedical transport problems that require numerous physical effects, like blood flow modelling and nanoparticle transport. Additionally, when they have a direct connection to physical systems, subjects like parameter analysis, optimization, and reduced-order models will be covered.

This work is intended for researchers, engineers, and graduate students studying applied mathematics, mechanical and biomedical engineering, and related subjects. It aims to be a useful tool for understanding the obvious and practical ways that data-driven methodology and numerical methods might be integrated. The primary goal is to present modeling methods that are both physically significant and computationally feasible.

All things considered, this book will offer a useful and methodologically sound resource for anyone interested in creating trustworthy hybrid modelling frameworks for a range of industrial systems. It plans to be a sensible tool for improving empirical multiphysics research and engineering design because it combines theory, computation, and data-assisted prediction into a single publication.

Coverage

Submission of book chapters should be focused on the following key highlights:

Section I: Fundamentals
1. Introduction to Multiphysics Systems
2. Governing Equations in Fluid Flow and Heat Transfer
3. Numerical Methods for Coupled Systems
4. Overview of Data-Driven Modelling Techniques

Section II: AI Techniques in Numerical Modeling

5. Artificial Neural Networks in Differential Equation Modelling
6. Regression and Response Surface Methods
7. Surrogate Modelling for Engineering Applications
8. Physics-Informed Neural Networks: Formulation and Implementation

Section III: Fluid Flow and Thermal Systems

9. AI-Based Modelling of Boundary Layer Flows
10. Magnetohydrodynamic Flow Analysis Using Data-Driven Methods
11. Heat Transfer Enhancement in Nanofluids
12. Coupled Flow and Thermal Systems: Numerical and Data-Driven Perspectives

Section IV: Biofluid and Biomedical Applications

13. Modeling of Blood Flow Using Numerical and AI Techniques
14. Nanoparticle Transport in Biological Systems
15. Bio-Convection with Microorganisms
16. Applications in Drug Delivery Systems

Section V: Optimization and Advanced Methods

17. Parameter Estimation Using Machine Learning
18. Hybrid Numerical–Data Driven Models
19. Uncertainty Analysis in Multiphysics Systems
20. Reduced-Order and Surrogate Models

Section VI: Industrial and Applied Case Studies

21. Applications in Energy Systems
22. Microfluidic and Lab-on-Chip Devices
23. Environmental and Transport Modelling
24. Computational Case Studies with Validation


Important Dates:
Abstract Submission (200 – 300 words) Deadline: Nov 30th, 2026
Notification of Acceptance: Dec 30th, 2026
Full Chapter (5,000 – 6,000 words) Submission Due: On or before Feb 27th, 2027

Submission procedure:
We invite researchers and practitioners to contribute original chapters to this book. Prospective authors are encouraged to submit a one-page chapter proposal or abstract outlining the proposed chapter’s content, objectives, and methodology by Nov 30th, 2026. Please include the chapter title and author details within the proposal. Authors will be notified regarding the acceptance of their proposals by Dec 30th, 2026. Accepted authors will be required to submit completed chapters of 18 to 22 pages by Feb 27th, 2027. All submitted chapters will undergo a rigorous peer-review process.

To submit your proposal or full-length chapter, please send a Word document attachment to editors: nanofluid.sahin@gmail.com, silpi.hazarika@rgu.ac.in
, dhananjayadav@gmail.com, mhmscd@mmmut.ac.in



Authors must refer to the following link for detailed guidelines for chapter preparation:
https://appleacademicpress.com/download/AAP_MS_INSTRUX.pdf



Note: Authors submitting manuscripts to this book do not incur any publication fees. To ensure the originality and quality of the content, all submissions must be previously unpublished and not under consideration for publication in any other venue.



About the Authors / Editors:
Editors: Prof. (Dr.) Sahin Ahmed

Prof. (Dr.) Sahin Ahmed is a Professor of Mathematics and the Dean of the Faculty of Engineering and Technology at Rajiv Gandhi University (RGU), Arunachal Pradesh, India. He holds a Ph.D. in Mathematics from Gauhati University (2005) and a M.Sc. from the University of Delhi (1994). With nearly three decades of academic and administrative experience, including a tenure as Head of the Department of Mathematics at RGU, his primary research fields span Computational Fluid Dynamics (CFD), Magnetohydrodynamics (MHD), heat and mass transport in porous media, nanofluids, and numerical modeling using tools like MATLAB and Mathematica. A prolific researcher, Prof. Ahmed has authored over 118 research publications in high-impact international journals, establishing significant contributions to the fields of applied mathematics and computational engineering.

Dr. Silpi Hazarika

Dr. Silpi Hazarika is an Assistant Professor in the Department of Mathematics at Mahapurusha Srimanta Sankaradeva Viswavidyalaya (MSSV), Nagaon, Assam, India. She earned her Ph.D. in Applied Mathematics in September 2021, specializing in computational fluid dynamics and thermal engineering. Her primary research interests focus on Magnetohydrodynamic (MHD) flows, heat and mass transfer in porous media, micropolar fluids, and the numerical modeling of nanofluids. An active researcher and collaborator, her scientific work on nanoparticle behavior and boundary layer flows is well-published in prominent international journals in the fields of applied mathematics and computational mechanics.

Dr. Dhananjay Yadav

Dr. Dhananjay Yadav received his Ph.D. degree from Department of Mathematics, Indian Institute of Technology (IIT) Roorkee, India in 2013 and post-graduation (M.Sc.) in Mathematics from DDU University Gorakhpur, India in 2007. Currently, he is working as an Associate Professor in Department of Mathematics at University of Nizwa, Oman. Prior to his appointment to University of Nizwa, Oman, he had worked as Principal Research Scientist at Athabasca University, Canada, Yonsei University, South Korea and Jeju National University, South Korea. He is a leading expert in CO2 capture, storage and oil recovery, Computational sustainability and environmental analytics, Fluid mechanics, Numerical analysis, Hydrodynamic and Hydromagnetic stability, Nanofluids and Fluid flow in porous media. He has published more than 180 research articles (high impact factor) in various reputed international journals. He is also listed in the top 2% influential researchers in the World prepared by Stanford University based on Scopus data from 2020 onward and ranked 2nd worldwide in Porous Medium research, placing him among the top 0.05% of scholars according to ScholarGPS.

Dr. Mohammad Hassan

Dr. Mohammad Hassan is an Associate Professor in the Department of Mathematics and Scientific Computing at Madan Mohan Malaviya University of Technology (MMMUT), Gorakhpur, Uttar Pradesh, India. He earned his Ph.D. in Mathematics from the prestigious Indian Institute of Technology Guwahati (IIT Guwahati) and successfully qualified the CSIR NET-JRF. With a strong background in applied mathematics, his primary research interests focus on Computational Fluid Dynamics, water wave mechanics, fluid-structure interaction, and analytical solutions for non-linear differential equations. An active researcher and doctoral supervisor, Dr. Hassan has contributed significantly to the mathematical modeling of physical systems, and his scholarly work is regularly published in reputed international journals specializing in computational science and engineering mathematics.




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