发现神经符号回归的网络动态.
Zihan Yu1, Jingtao Ding2, Yong Li3
1Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
Nature computational science
|October 23, 2025
概括
本研究引入了一个神经符号回归方法,自动发现数据中复杂网络动态的数学模型. 这种方法增强了对系统的理解,并推进了复杂性科学.
科学领域:
- 复杂性科学 复杂性科学
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 分析高维复杂系统需要了解它们的网络动态.
- 这些系统的数学模型很少,尽管数据丰富,但分析是有限的.
- 现有的模型往往缺乏明确的基本原则.
研究的目的:
- 从观测数据中自动推导网络动态的数学公式的方法.
- 在复杂系统中弥合数据可用性和模型开发之间的差距.
- 提高复杂系统的理解和预测能力.
主要方法:
- 使用神经符号回归方法自动从数据中推导公式.
- 将高维网络搜索减少到相当的单维系统.
- 使用预训练的神经网络来指导准确的公式发现.
主要成果:
- 成功恢复了十个基准系统的正确形式和参数.
- 改善了基因调节 (59.98%) 和微生物群落 (55.94%) 的预测准确性.
- 发现了人类移动网络上的流行病传播动态,揭示了规模不变性和国家级干预效应.
结论:
- 网络动态的机器驱动发现显著提高了对复杂系统的理解.
- 这种方法通过从数据中创建模型来推进复杂性科学的领域.
- 该方法在理论和实证系统中显示了广泛的适用性.
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