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    The American Society of Mechanical Engineers

    Special Issue on Artificial Intelligence and Machine Learning for Thermal Science and Engineering

    Journal of Heat and Mass Transfer
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    Artificial intelligence (AI) is transforming how researchers analyze, design, and optimize thermal materials, processes and systems. This special issue focuses on emerging advances in AI for thermal science and engineering. It aims to highlight how data-driven methods, physics-informed learning, large language models, and hybrid AI–physics frameworks are reshaping the analysis, design, optimization, and control of thermal systems across multiple scales. The special Issue seeks original research and review articles that advance both methodological foundations and practical applications. By bringing together researchers across thermal science, computation, and data science, it will help define the future role of AI/ML in accelerating discovery and engineering innovation in thermal systems.

    Topics of interest include, but are not limited to, AI/ML for heat and mass transfer, thermal transport in materials, multiphase and reactive flows, energy conversion and storage, thermal system design, digital twins, inverse problems, uncertainty quantification, scientific machine learning, and autonomous or closed-loop experimentation. Contributions that integrate domain knowledge, first-principles modeling, and experimental data are especially encouraged.. 

    Topic Areas

    THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:

    • Physics-informed learning for multiscale thermal transport and materials
    • AI for multiphase flows and phase-change heat transfer
    • Deep learning for turbulence and reactive flow modeling
    • Digital twins and autonomous experimentation in thermal science
    • Inverse design, generative AI and optimization of thermal systems  
    • Multi-fidelity data fusion, uncertainty quantification, and data assimilation
    • AI-augmented experiments and AI-enabled diagnostics and imaging
    • AI for energy conversion, storage, and thermal management
    • Large language models and AI agents for thermal research
    • Reinforcement learning for flow control and thermal management

    Special Issue Publication Dates


    Paper submission deadline: October 31, 2026
    Initial review completed: January 27, 2027
    Publication date: June 2027

    Submission Instructions

    Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here. 

    Once at the Paper Submittal page, select the Journal of Heat and Mass Transfer, and then select the Special Issue on Artificial Intelligence and Machine Learning for Thermal Science and Engineering.

    Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.

    Guest Editors

    Guoping Xiong, University of Texas, Dallas (Guoping.Xiong@UTDallas.edu)

    Tengfei Luo, University of Notre Dame (tluo@nd.edu)

    Ming Hu, University of South Carolina (HU@sc.edu)

    Masato Ohnishi, The Institute of Statistical Mathematics, Japan (masato.ohnishi.ac@gmail.com)

    Jianxun Wang, Cornell University (jw2837@cornell.edu)