Machine Learning Approaches for Heat Transfer Prediction

Recent developments in machine learning (ML) are transforming heat transfer by providing game-changing answers to persistent problems in system optimization and thermal modeling. The data-driven methods for heat transfer prediction are thoroughly examined in this chapter that methodically connects basic theory with cutting-edge applications. Starting with the fundamental mathematical underpinnings of both heat transfer and ML, we develop a strict framework for combining both fields. The chapter then discusses important approaches, with a focus on their benefits over traditional computational techniques, such as deep neural networks (convolutional neural networks, recurrent neural networks), supervised learning techniques (support vector regression, Gaussian processes), and physics-informed machine learning. From microelectronics cooling to large-scale energy systems, a substantial amount of the conversation centers on novel applications across several scales and domains, emphasizing groundbreaking outcomes in computing efficiency and predictive accuracy. Emerging paradigms like digital twins for thermal management, explainable artificial intelligence in heat transfer applications, and hybrid ML–computational fluid dynamics modeling are given particular attention. This chapter not only summarizes the state-of-the-art but also provides practical guidance for applying ML solutions to heat transfer problems. It is a reference for practitioners looking to use ML for next-generation thermal science and engineering applications as well as an instructional tool for those new to the topic. This chapter offers research directions while critically analyzing the current constraints in relation to real-time implementation, model generalizability, and data needs. It also includes recommendations for algorithm selection, validation procedures, and performance benchmarking.