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MLEng期刊研究路线图|2026年智能制造中的人工智能与机器学习路线图

28 Aug 2026 gabriels

The need for microsecond speed machine learning (ML) inference for particle physics experiments has emerged in recent years, in particular for the forthcoming upgrades to the experiments at the Large Hadron Collider at CERN. A community has grown around the need to develop the custom hardware platforms and tools required. The material presented in this report is drawn from the latest workshop held by the fast ML for science community and comprises of a collection of perspectives on the status of fast ML in different scientific domains, and the supporting technology.

文章介绍

2026 roadmap on artificial intelligence and machine learning for smart manufacturing

Jay Lee, Hanqi Su, Marco Macchi, Adalberto Polenghi, Wei Wu, Zhiheng Zhao, George Q Huang, Kiva Allgood, Devendra Jain, Benedikt Gieger, Vibhor Pandhare, Soumyabrata Bhattacharjee, Ram Mohril, Lingbao Kong, Qiyuan Wang, Xinlan Tang, Sungjong Kim, Chan Hee Park, Byeng D Youn, Guo Dong Goh, Xi Huang, Wai Yee Yeong, Yung C Shin, He Zhang, Zitong Wang, Fei Tao, Jagjit Singh Srai, Satyandra K Gupta, Byung Gun Joung, Albin John, John W Sutherland, Sang Won Lee, Olga Fink, Vinay Sharma, Faez Ahmed, Wei ‘Wayne’ Chen, Mark Fuge, Arild Waaler, Martin G Skjæveland, Dimitris Kyritsis, Wei Chen, Vispi Nevile Karkaria, Yi-Ping Chen, Ying-Kuan Tsai, Joseph Cohen, Xun Huan, Jing (Janet) Lin, Liangwei Zhang, Gregory W Vogl, Aaron W Cornelius, Xiaodong Jia, Dai-Yan Ji, Takanobu Minami and Ruoxin Wang

客座编辑：

Jay Lee，美国马里兰大学

期刊介绍

Machine Learning: Engineering

Machine Learning: Engineering是一本多学科开放获取期刊，致力于在所有工程领域应用机器学习、人工智能数据驱动的计算方法。该期刊还发表介绍机器学习和人工智能在方法、理论或概念上的进展，并将其应用于所有工程领域的研究。