Learning interpretable network dynamics via universal neural symbolic regression

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jiao Hu, Jiaxu Cui, Bo Yang
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引用次数: 0

Abstract

Discovering governing equations of complex network dynamics is a fundamental challenge in contemporary science with rich data, which can uncover the hidden patterns and mechanisms of the formation and evolution of complex phenomena in various fields and assist in decision-making. In this work, we develop a universal computational tool that can automatically, efficiently, and accurately learn the symbolic patterns of changes in complex system states by combining the excellent fitting capability of deep learning with the equation inference ability of pre-trained symbolic regression. We perform extensive and intensive experimental verifications on more than ten representative scenarios from fields such as physics, biochemistry, ecology, and epidemiology. The results demonstrate the remarkable effectiveness and efficiency of our tool compared to state-of-the-art symbolic regression techniques for network dynamics. The application to real-world systems including global epidemic transmission and pedestrian movements has verified its practical applicability. We believe that our tool can serve as a universal solution to dispel the fog of hidden mechanisms of changes in complex phenomena, advance toward interpretability, and inspire further scientific discoveries.

Abstract Image

通过通用神经符号回归学习可解释的网络动力学
发现复杂网络动力学的控制方程是当代丰富数据科学的基本挑战,它可以揭示各个领域复杂现象形成和演化的隐藏模式和机制,为决策提供帮助。在这项工作中,我们开发了一种通用的计算工具,通过将深度学习的优秀拟合能力与预训练的符号回归的方程推理能力相结合,可以自动,高效,准确地学习复杂系统状态变化的符号模式。我们在物理学、生物化学、生态学和流行病学等领域的十多个代表性场景中进行了广泛而深入的实验验证。结果表明,与网络动力学的最先进的符号回归技术相比,我们的工具具有显着的有效性和效率。通过对全球流行病传播和行人运动等现实系统的应用,验证了该方法的实用性。我们相信,我们的工具可以作为一种通用的解决方案,消除复杂现象中隐藏的变化机制的迷雾,向可解释性迈进,并激发进一步的科学发现。
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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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