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Call for Papers for a Special Issue on "Innovative Multiscale Optimization and AI-Enhanced Simulation for Advanced Engineering Design and Manufacturing"
- 详细资料
- 发布于 2025年7月22日 星期二 14:12
Guest Editors:
Prof. Dr. Sohail Nadeem (Lead GE)
Quaid-I-Azam University
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Prof. Dr. Sohail Nadeem currently works at the Department of Mathematics, Quaid-i-Azam University. His ISI papers and citations are over 25000. He supervised 34 Ph.D and more than 115 M.Phil graduates. He has received many international and national awards. He is a fellow of the Pakistan Academy of Sciences and a young fellow of the World Academy of Sciences. His main research areas are Applied Mathematics, Differential Equations, Fluid Mechanics, Blood Flow, Peristaltic flows, Nano fluids.
Dr. Sajjad Ur Rehman
Quaid-I-Azam University
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Dr. Sajjad Ur Rehman is currently an Assistant Professor in the Department of Mathematics at Quaid-I-Azam University, Islamabad, Pakistan. Prior to this, he was a Postdoctoral Fellow at Sogang University, Seoul, South Korea, from September 2019 to July 2020, where he focused on numerical simulations using the commercial software Star-CCM+. His research interests include Numerical Simulation; Finite Difference method; Fourier Analysis; Projection method; Direct Numerical Simulation; Physics of particle-laden flow; Numerical investigation of polymer-laden turbulence.
Dr. Noreen Sher Akbar
Prince Mohammad Bin Fahd University
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Dr. Noreen Sher Akbar is a Research Professor at the Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Saudi Arabia, a position she has held since June 2024. Dr. Akbar has an impressive research portfolio, with over 370 publications in internationally reputed journals, accumulating an impact factor exceeding 2000 and more than 15,000 citations. Her H-index stands at 68, reflecting the significant influence of her work in the field. Her research interests include nanofluids, Newtonian and non-Newtonian fluid dynamics, peristaltic flows, fluid interactions in arteries, porous media, and magnetohydrodynamic (MHD) flows.
Background and Motivation:
The field of engineering design optimization is rapidly advancing, driven by new computational techniques, artificial intelligence (AI), and the application of multiscale methods. As industries such as aerospace, automotive, and manufacturing increasingly demand more efficient, cost-effective, and sustainable solutions, the integration of optimization techniques with modern AI, machine learning (ML), and simulation methods offers unprecedented opportunities. This special issue seeks to bring together the latest research on AI-driven design optimization, multiscale simulation, and their industrial applications. We invite original papers that highlight innovative advancements in these fields, with a focus on their real-world engineering applications.
This special issue welcomes contributions that address both theoretical and applied aspects of engineering design optimization, particularly in the context of modern AI techniques and multiscale methods. Submissions are invited that explore, but are not limited to, the following topics:
- Multiscale Optimization and Simulation Techniques
- Development of multiscale methods for material and structural optimization (nano, micro, and macro scales).
- Optimization strategies for advanced manufacturing techniques like additive manufacturing (3D printing), composite materials, and nanomaterials.
- Coupled simulations at multiple scales for complex systems, including simulations of material behavior, structural integrity, and performance under varying conditions.
- Artificial Intelligence and Machine Learning in Optimization
- Application of machine learning algorithms (e.g., deep learning, reinforcement learning) in optimization tasks, from design space exploration to decision-making.
- Integration of AI-driven surrogate models for fast and accurate predictions in complex optimization problems.
- Data-driven optimization methods in material design, aerospace engineering, automotive systems, and other industrial applications.
- Optimization for Advanced Manufacturing Processes
- Optimization of processes such as additive manufacturing, composite material fabrication, and smart manufacturing systems.
- The role of optimization in designing for manufacturing (DFM) and ensuring product quality through process optimization.
- Case studies on industrial applications of simulation-based optimization in modern manufacturing environments.
- Dynamic and Vibration-Based Optimization
- Optimization methods for dynamic systems and vibration control in structures and mechanical systems.
- Applications in automotive design (e.g., NVH—noise, vibration, and harshness), aerospace components, and civil engineering structures.
- Design of resilient structures and materials with improved vibration damping and dynamic load-bearing capacities.
- Industrial Applications and Case Studies
- Real-world applications of optimization, simulation, and AI in industries such as aerospace, automotive, energy, and manufacturing.
- Integration of optimization methods with product lifecycle management (PLM) tools for efficient design and production.
- Practical case studies of multiscale, AI-enhanced optimization techniques applied to solve industry-specific challenges.
Objectives:
The primary goal of this special issue is to bridge the gap between advanced theoretical optimization methods and practical engineering applications. We aim to highlight the latest advancements in multiscale optimization, AI-driven approaches, and their applications in manufacturing and design. By bringing together state-of-the-art research and real-world case studies, this issue will contribute to the development of more efficient, sustainable, and innovative engineering solutions.
Submission Deadline: August 31, 2026
Call for Papers for a Special Issue on "Recent Advances in Hyperparameter Tuning for Machine Learning Models"
- 详细资料
- 发布于 2025年6月25日 星期三 08:56
Guest Editors:
Dr. Hai-Canh VU
Roberval Laboratory, Compiègne University of Technology, 60200 Compiègne, France
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Interests: predictive maintenance; prognostics and health management; machine learning; Industry 4.0
Dr. Nassim Boudaoud
Roberval Loboratory, Compiègne University of Technology, 60200 Compiègne, France
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Interests: statistical process control; prognostics and health management; machine learning; Industry 4.0
Background and Motivation:
In recent years, machine learning (ML) has demonstrated significant breakthroughs across a wide range of applications—from computer vision and natural language processing to biomedical engineering and industrial systems. However, the performance of ML models often hinges critically on the choice of hyperparameters, such as learning rates, regularization terms, and network architectures. Improper tuning can lead to suboptimal performance, overfitting, or excessive computational costs.
Hyperparameter tuning, therefore, remains a persistent and non-trivial challenge in both academic research and industrial practice. While traditional methods such as grid search and random search have been widely used, they are often inefficient or infeasible for large-scale models. Recently, advanced approaches—including Bayesian optimization, evolutionary strategies, gradient-based tuning, and meta-learning—have gained traction and promise more effective solutions.
This special issue aims to bring together cutting-edge research that addresses the theoretical, computational, and practical challenges of hyperparameter optimization in machine learning. The goal is to foster novel contributions that advance the automation, efficiency, and robustness of model tuning in diverse domains.
Topics of Interest:
- Novel algorithms for hyperparameter optimization (HPO)
- Bayesian optimization, bandit methods, and surrogate models for HPO
- Population-based and evolutionary strategies
- Differentiable and gradient-based hyperparameter learning
- Multi-objective and cost-aware tuning strategies
- AutoML frameworks and systems for scalable tuning
- Hyperparameter transfer learning and warm-starting
- HPO in deep learning and reinforcement learning
- Domain-specific tuning (e.g., for healthcare, finance, robotics)
- Interpretability and explainability in HPO
- Benchmarks, datasets, and empirical comparisons of tuning methods
- Integration of HPO in federated and distributed learning
- Applications of HPO in real-world industrial settings
Target Audience:
The special issue will be of interest to:- Researchers in machine learning, optimization, and artificial intelligence
- Practitioners developing or deploying ML models at scale
- Developers of AutoML platforms and hyperparameter tuning tools
- Applied scientists and engineers in fields such as healthcare, manufacturing, finance, and environmental science
Submission Deadline: November 15, 2025
Call for Papers for a Special Issue on "AI-Driven Simulation and Neural Optimization for Smart Systems and Healthcare Applications"
- 详细资料
- 发布于 2025年6月06日 星期五 12:54
Guest Editors:
Assoc. Prof. Dr Hoshang Kolivand
Head of Applied Computing Research Group
IEEE senior member
Liverpool John Moores University, UK
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Dr. Ata Jahangir Moshayedi
School of Information Engineering, Jiangxi University of Science and Technology, No
86, Hongqi Ave, Ganzhou, 341000, Jiangxi, China
IEEE senior member
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Prof. Tanzila Saba
Associate Director, Research and Initiative Center
Leader of Artificial Intelligence and Data Analytics Lab
Research Professor | IEEE Senior Member | HEA Fellow (UK)
Prince Sultan University, Riyadh, Saudi Arabia
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Dr. Shamsollah Ghanbari
Assistant Professor of Computer Science
Islamic Azad University, Iran
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Dr. Mohammed Ibrahim Khalaf
Dean, College of Science
Head of Quality Assurance and Academic Accreditation Unit
AL-Maarif University College, Iraq
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Background and Rationale:
Simulation has long played a foundational role in system design, optimisation, and predictive analysis across a range of engineering and scientific domains. In recent years, the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques into simulation pipelines has transformed traditional modelling paradigms. These intelligent systems can now automatically learn from data, optimise parameter spaces, adapt to real-time input, and deliver enhanced predictive capabilities in complex, uncertain environments.
In parallel, the advancement of neural network architectures—including deep learning, recurrent networks, and neuro-symbolic hybrids—has enabled the development of intelligent simulation frameworks capable of mimicking dynamic systems with unprecedented accuracy and computational efficiency. From digital twin platforms to healthcare simulators, these models allow for real-time interaction, adaptive control, and robust optimisation in domains where traditional approaches fall short.
This Special Issue aims to explore the next generation of simulation-driven design and decision-making, focusing on how AI-enhanced modelling, neural computation, and intelligent optimisation are being applied to real-world problems in healthcare, engineering design, smart environments, and cyber-physical systems.
Scope and Topics:
This Special Issue invites original research, review articles, and case studies related to (but not limited to):
- Simulation-based design optimisation using ML/AI
- Multi-objective and metaheuristic algorithms for smart systems
- Neural network-driven simulation frameworks
- AI and BCI systems integrated with virtual simulation
- Intelligent healthcare simulation models
- Deep learning models for dynamic system response prediction
- Digital twin systems and predictive modelling
- AI-assisted diagnostics and optimisation in biomedical systems
- Real-time simulation for autonomous system control
- Hybrid AI models for engineering simulation and decision support
Correspondence:
Lead Guest Editor
Assoc. Prof. Dr. Hoshang Kolivand
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Submission Deadline: January 1st, 2026
Call for Papers for a Special Issue on "Multi-modal Information Learning and Analytics on Cross-Media Data Integration"
- 详细资料
- 发布于 2024年11月28日 星期四 14:44
Guest Editors:
Xu Zheng Shanghai Polytechnic University, China Xu Zheng is currently the professor in Shanghai Polytechnic University, China. He is also the associate editor of Springer ECR journal and Springer DIoT journal.
Jemal H. Abawajy Deakin University, Australia Jemal H. Abawajy is a Full Professor with the School of Information Technology, Faculty of Science, Engineering, and Built Environment, Deakin University, Australia. He is currently the Director of the Parallel and Distributing Computing Laboratory.
Shafi’i Muhammad Abdulhamid Federal University of Technology, Minna-Nigeria Shafi’i Muhammad Abdulhamid received his Ph.D. in Computer Science from University of Technology Malaysia (UTM). Presently, he is the professor of FEDERAL UNIVERSITY OF TECHNOLOGY, MINNA-NIGERIA.
Haruna Chiroma University of Hafr Al Batin, Saudi Arabia Haruna Chiroma received the Ph.D. degree from the Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya. He is currently Professor (Assistant) at University of Hafr Al Batin.
Aims and Scope of the Special Issue
We are living in the era of data deluge. Meanwhile, the world of big data exhibits a rich and complex set of cross-media contents, such as text, image, video, audio and graphics. Thus far, great research efforts have been separately dedicated to big data processing and cross-media mining, with well theoretical underpinnings and great practical success. However, studies jointly considering cross-media big data analytics are relatively sparse. This research gap needs our more attention, since it will benefit lots of real-world applications. Despite its significance and value, it is non-trivial to analyze cross-media big data due to their heterogeneity, large-scale volume, increasing size, unstructured, correlations, and noise. Multi-modal Information Learning, which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved terrific progress in both academy and industry.
This special issue focuses on learning methods to achieve high performance Multi-modal Information analysis and understanding under uncontrolled environments in large scale, which is also a very challenging problem. Moreover, it attracts much attention from both the academia and the industry. We hope this topic will aggregate top level works on the new advances in Multi-modal Information from cross-media data.
Topics of interests include, but are not limited to:
- Cross-Media Big Data Representation
- Large-scale multimodal media data acquisition
- Novel dataset and benchmark for cross-media big data analytics
- Cross-Media Big Data Management
- Large-scale multimodal information fusion
- Domain adaptation for cross-media big data
- Cross-media big data organization, retrieval and indexing
- Learning methods to bridge the semantic gap among media types
- Cross-Media Big Data Understanding and Applications
- Multi-modal Information for feature representation
Submissions for this special issue should be submitted through the Journal’s submission system, Editorial Manager. Detailed guidelines on submission format and process can be found in the Instructions for Authors of the journal.
Submission Deadline: October 1, 2025
DOAJ Seal awarded to International Journal for Simulation and Multidisciplinary Design Optimization demonstrating “best practice in open access publishing”
- 详细资料
- 发布于 2021年3月23日 星期二 10:28
We are pleased to announce that International Journal for Simulation and Multidisciplinary Design Optimization, among 22 other EDP Sciences’ journals, has recently been awarded the DOAJ Seal which “is awarded to journals that demonstrate best practice in open access publishing”. Only around 10% of journals indexed in the DOAJ are awarded the DOAJ Seal, so it is a gratifying mark of recognition of the excellent work International Journal for Simulation and Multidisciplinary Design Optimization is doing.
Call for Papers for a Special Issue on "Advances in Modeling and Optimization of Manufacturing Processes"
- 详细资料
- 发布于 2021年1月12日 星期二 16:11
Guest Editors:
Dr. Sachin Salunkhe
Vel Tech Rangarajan, Institute of Science and Technology, Chennai, India
Prof. Sofiane Guessasma
INRA, Paris, France
Dr. Vishal Naranje
Amity University, Dubai, United Arab Emirates
Call for Papers for a Special Issue on "Computation Challenges for engineering problems"
- 详细资料
- 发布于 2020年12月21日 星期一 14:37
Guest Editors:
Dr. Nadhir LEBAAL
University of Technology (UTBM), France
Dr. Subramanian JEYANTHI
School of mechanical Engineering
Vellore Institute of Technology, India
Dr. Jebaseelan DAVIDSON
School of mechanical Engineering
Vellore Institute of Technology, India
Dr. M.C. LENINBABU
School of mechanical Engineering
Vellore Institute of Technology, India
Call for Papers for a Special Issue on "Simulation and Optimization for Industry 4.0"
- 详细资料
- 发布于 2020年11月19日 星期四 10:15
Guest Editors:
Professor Abdelkhalak EL HAMI
Normandy University, National institute on applied sciences INSA- Rouen-Normandy, France
Professor Mohamed HADDAR
ENISfax, Sfax, Tunisie
Professor Bouchaib RADI
FST Settat, Morocco
Professor Norelislam EL HAMI
ENSA Kenitra, Morocco
The "International Journal of Simulation and Multidisciplinary Design Optimization (IJSMDO)" is now indexed in Scopus
- 详细资料
- 发布于 2017年9月11日 星期一 11:00
EDP Sciences are pleased to announce that International Journal for Simulation and Multidisciplinary Design Optimization (IJSMDO) has been accepted for indexation in Elsevier’s Scopus database.
Scopus is widely recognised as one of the largest abstract and citation databases of scholarly literature. The bibliographic database provides an overview of global research output in STEM fields as well as the social sciences, arts, and humanities. Scopus provides a suite of search and analysis features that enhance journal discoverability for an audience of over 3,000 academic, government, and corporate institutions globally.
Ariana Fuga, Senior Publishing Editor at EDP Sciences, commented on the announcement: “This is an important step for the visibility of the journal, and we are proud that International Journal for Simulation and Multidisciplinary Design Optimization has been approved for inclusion by the independent advisory board.”
Call for Papers for a special issue on "Uncertainty-Based Design Optimization"
- 详细资料
- 发布于 2017年2月09日 星期四 16:00
Guest Editor: Professor Xiao-Jun Wang, Beihang University, China
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Editor: Professor Chao Jiang, Hunan University, China
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Submission deadline - 20th April 2017