With the continued development of artificial intelligence technology, machine learning has become an important method of predictive analysis. In particular, coupled heat transfer analysis based on machine learning has significant advantages in terms of reducing experimental costs and modeling time. Through machine learning, researchers can rapidly determine the coupled heat transfer processes between temperature fields, flow fields, and stress fields. Furthermore, machine learning can predict the results of experiments using established models with existing experimental data. This means that researchers can predict heat transfer performance under different conditions without actually experimenting, reducing the cost and time required for experiments. In addition, modeling methods based on machine learning can directly determine the relationship of heat transfer processes by training the models, thus avoiding complex mathematical derivations and calculation processes.
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.
Research areas within the scope of this collection include, but are not limited to:
• 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
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.
With the continued development of artificial intelligence technology, machine learning has become an important method of predictive analysis. In particular, coupled heat transfer analysis based on machine learning has significant advantages in terms of reducing experimental costs and modeling time. Through machine learning, researchers can rapidly determine the coupled heat transfer processes between temperature fields, flow fields, and stress fields. Furthermore, machine learning can predict the results of experiments using established models with existing experimental data. This means that researchers can predict heat transfer performance under different conditions without actually experimenting, reducing the cost and time required for experiments. In addition, modeling methods based on machine learning can directly determine the relationship of heat transfer processes by training the models, thus avoiding complex mathematical derivations and calculation processes.
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.
Research areas within the scope of this collection include, but are not limited to:
• 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
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.