Advances in Heat and Mass Transfer: Integrating Numerical Methods with Artificial Intelligence, Machine Learning, and Data-Driven Approaches

Submission Deadline: 10 October 2025 View: 328 Submit to Special Issue

Guest Editors

Prof. Samad Noeiaghdam

Email: snoei@hnas.ac.cn

Affiliation: Institute of Mathematics, Henan Academy of Sciences, No. 228, Chongshili, Zhengdong New District, Zhengzhou, 450046, China

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Research Interests: MHD, heat and mass transfer

Prof. Unai Fernandez-Gamiz

Email: unai.fernandez@ehu.eus

Affiliation: Department of Fluids Mechanics, University of the Basque Country, Plaza Europa 1, Donostia-San Sebastian, 20018, Spain

Homepage:

Research Interests: MHD, heat and mass transfer, fluid mechanics

Summary

Heat and mass transfer are fundamental processes in numerous engineering, environmental, and industrial applications, ranging from energy systems and thermal management to climate modeling and biomedical engineering. With the growing complexity of these systems, traditional numerical methods face challenges in accuracy, computational efficiency, and scalability. The integration of Artificial Intelligence (AI), Machine Learning (ML), and data-driven techniques with numerical methods has emerged as a transformative approach to address these challenges. This special issue aims to explore the latest advancements in the intersection of heat and mass transfer, numerical methods, and AI/ML algorithms, fostering innovation and interdisciplinary collaboration.
The scope of this special issue includes the development and application of AI/ML techniques to enhance the prediction, optimization, and control of heat and mass transfer phenomena. Contributions are encouraged on novel numerical methods, hybrid models, and data-driven approaches that leverage AI/ML to solve complex problems in thermal and fluid systems. This issue will provide a platform for researchers to share cutting-edge methodologies, case studies, and applications, driving the future of this rapidly evolving field.

This special issue seeks to:
1. Highlight the integration of AI, ML, and data-driven techniques with numerical methods for heat and mass transfer problems.
2. Showcase innovative approaches to improve computational efficiency, accuracy, and scalability in modeling and simulation.
3. Explore real-world applications of AI/ML in energy systems, environmental engineering, manufacturing, and biomedical fields.
4. Foster interdisciplinary research by bridging the gap between traditional numerical methods and emerging AI/ML technologies.

Suggested Themes:
1.  AI/ML-enhanced numerical methods for heat and mass transfer.
2. Data-driven modeling and optimization of thermal systems.
3. Hybrid approaches combining physics-based models and machine learning.
4. Applications of deep learning in fluid dynamics and heat transfer.
5. Uncertainty quantification and sensitivity analysis using AI/ML.
6. AI-driven design and control of heat exchangers and thermal systems.
7. Machine learning for phase change and multiphase flow problems.
8. Predictive modeling of mass transfer in environmental and biological systems.
9. Reinforcement learning and AI for real-time thermal management.
10. Benchmarking and validation of AI/ML models in heat and mass transfer.

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Published Papers