Coupled Heat Transfer and Applications of Machine Learning

The field of coupled heat transfer has seen significant advancements with the integration of machine learning techniques. Traditionally, analyzing coupled heat transfer processes requires extensive experimental setups and complex mathematical modeling, which is both time-consuming and costly. However, machine learning has revolutionized this field by offering predictive analysis capabilities that significantly reduce these burdens.

Current research highlights the ability of machine learning to efficiently model the interactions between temperature fields, flow fields, and stress fields, providing accurate predictions without the need for exhaustive experimentation. Despite these advancements, gaps remain in understanding the full potential of machine learning in this context, particularly in optimizing algorithms for better accuracy and efficiency. Ongoing research focuses on the reliability of machine learning models in diverse conditions and the need for more robust verification methods. As the field progresses, there is a pressing need for comprehensive studies that address these challenges and explore the untapped potential of machine learning in coupled heat transfer analysis.

This Research Topic aims to explore advances in coupled heat transfer analysis based on machine learning. The goal is to provide a platform for researchers to address challenges in the applications of machine learning in coupled heat transfer and explore innovative solutions. Specifically, the Research Topic seeks to advance the understanding of the optimization of intelligent algorithms, the accuracy of simulations, and the integration of multiphysics coupling analysis.

To gather further insights into the integration of machine learning with coupled heat transfer analysis, we welcome articles addressing, but not limited to, the following themes:
• Intelligent algorithm optimization of coupled heat transfer
• Simulation of heat transfer processes
• Experimental and simulated verification
• Multiphysics coupling analysis
• Optimization of heat transfer in complex structures.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: coupled heat transfer, heat transfer processes, machine learning, artificial intelligence, algorithm optimization, simulated verification, experimental verification, multiphysics coupling analysis, Physics-informed neural network, Reacting flow

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.