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Updated: Sep 11, 2025

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电子核截面从转移学习的截面
Krzysztof M Graczyk1, Beata E Kowal1, Artur M Ankowski1
1University of Wrocław, Institute of Theoretical Physics, plac Maxa Borna 9, 50-204 Wrocław, Poland.
Physical review letters
|August 18, 2025
概括
转移学习使深度神经网络 (DNN) 能够适应新的物理问题. 经过电子-碳散射训练的DNN在微调后准确地预测其他核目标的截面.
科学领域:
- 核物理 核物理 核物理
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 转移学习是一种机器学习技术,在这种技术中,在一个任务上训练的模型被重新用于第二个相关任务.
- 深度神经网络 (DNN) 在各种科学领域都显得有前途.
研究的目的:
- 在核物理中研究使用DNN转移学习的应用.
- 评估在一个散射过程中训练的微调DNN的有效性,以预测相关过程的结果.
主要方法:
- 在包括电子-碳散射数据的深度神经网络中进行训练.
- 为新的预测任务微调训练好的DNN.
- 对各种核目标的实验横截面数据验证预测.
主要成果:
- 微调的DNN准确地预测了电子与核目标相互作用的截面.
- 在从-3到铁的各种核中成功应用转移学习.
结论:
- 转移学习是加速核物理预测的可行和有效技术.
- DNN可以有效地适应模拟各种电子核散射过程.
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