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Machine Learning for Thermal Transport

Thermal transport is a fundamental process that plays a key role in many applications, such as thermal management of electronic devices, energy conversion (e.g., thermoelectrics), and thermal insulation. Remarkable progress has been made in understanding and engineering thermal transport in recent decades across different scales. However, many challenges still exist, particularly for complex heat transfer problems. Machine learning is emerging as a powerful tool to tackle problems that are difficult to solve using traditional analytical, computational, or experimental approaches. This special topic aims to provide a timely forum for presenting and discussing the most recent advances in the development and application of machine learning to study thermal transport in applied physics.

Guest Editors: Ruiqiang Guo, Bing-Yang Cao, Tengfei Luo, and Alan McGaughey

Xiaonan Wang; Jinfeng Yang; Penghua Ying; Zheyong Fan; Jin Zhang; Huarui Sun
Yuchao Hua; Lingai Luo; Steven Le Corre; Yilin Fan
Yimu Lu; Yongbo Shi; Junyuan Wang; Haikuan Dong; Jie Yu
Yagyank Srivastava; Ankit Jain
Zihe Chen; Shilv Yu; Cheng Yuan; Kun Hu; Run Hu
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