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
