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MLST期刊首个研究路线图|科学领域快速机器学习路线图

06 May 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

Roadmap on fast machine learning for science

Sioni Summers, Alex Tapper, Thea Klæboe Årrestad, Chen Qin, Karin Rathsman, Matthew Streeter, Charlotte Palmer, Jonathan Citrin, Changgang Zheng, Noa Zilberman, Alexander Titterton and Tobias Becker

客座编辑：

Sioni Summers，欧洲核子研究中心
Alex Tapper，英国帝国理工学院

期刊介绍

Machine Learning: Science and Technology

2024年影响因子：4.6 Citescore：7.7
Machine Learning: Science and Technology （MLST）是一本跨学科期刊，致力于发表智能机器在物理、材料科学、化学、生物学、医学、地球科学、天文学和工程学等多学科领域的应用和发展。涉及领域包括：物理学和空间科学；设计和发现新材料和分子；材料表征技术；模拟材料、化学过程和生物系统；原子和粗粒度模拟；量子计算；生物学、医学和生物医学成像；地球科学（包括自然灾害预测）和气候学；模拟方法和高性能计算。同时，也包括机器学习方法在概念上的新进展：新的学习算法；深度学习架构；核心方法；概率和贝叶斯方法；生成方法；强化和主动学习；经常性和基于时间结构的方法；神经启发方法（包括神经形态计算）。