国家点火设施
点火系统
贝叶斯概率
核工程
融合
人工智能
计算机科学
物理
模拟
激光器
航空航天工程
惯性约束聚变
工程类
光学
语言学
哲学
作者
B. K. Spears,S. Brandon,D. T. Casey,J. E. Field,Jim Gaffney,Kelli Humbird,A. L. Kritcher,Michael Kruse,Eugene Kur,Bogdan Kustowski,S. Langer,D. H. Munro,R. Nora,J. L. Peterson,D. J. Schlossberg,Paul J. Springer,A. B. Zylstra
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2025-08-14
卷期号:389 (6761): 727-731
被引量:6
标识
DOI:10.1126/science.adm8201
摘要
An inertial confinement fusion experiment, carried out at the National Ignition Facility, has achieved ignition by generating fusion energy exceeding the laser energy that drove the experiment. Prior to the experiment, a generative machine learning model that combines radiation hydrodynamics simulations, deep learning, experimental data, and Bayesian statistics was used to predict, with a probability greater than 70%, that ignition was the most likely outcome for this shot.
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