Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent innovations in AI and machine learning uniquely offer the potential for collecting new types of physically meaningful features that have not been addressed in the past, for making their insights available to other domains, and for solving for physical quantities based on first principles for phase-change thermofluidic systems. This review outlines core ideas of current AI technologies connected to thermal energy science to illustrate how they can be used to push the limit of our knowledge boundaries about boiling and condensation phenomena. AI technologies for meta-analysis, data extraction, and data stream analysis are described with their potential challenges, opportunities, and alternative approaches. Finally, we offer outlooks and perspectives regarding physics-centered machine learning, sustainable cyberinfrastructures, and multidisciplinary efforts that will help foster the growing trend of AI for phase-change heat and mass transfer.

人工智能 (AI) 正在改变两相传热研究的范式。人工智能和机器学习领域的最新创新独特地提供了收集过去尚未解决的新型物理有意义特征的潜力，使它们的见解可用于其他领域，以及基于相的第一原理求解物理量。改变热流体系统。这篇综述概述了当前与热能科学相关的人工智能技术的核心思想，以说明如何利用它们来突破我们关于沸腾和冷凝现象的知识界限。描述了用于元分析、数据提取和数据流分析的人工智能技术及其潜在的挑战、机遇和替代方法。最后，我们提供了有关以物理为中心的机器学习、可持续网络基础设施和多学科努力的展望和观点，这些观点和观点将有助于促进相变传热传质人工智能的发展趋势。