用深度学习+符号回归自动发现复杂系统演化规律。
Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression
- 融合深度学习与预训练符号回归，自动推导系统动态方程。
- 在十余个跨领域场景中优于现有符号回归方法。
- 适合需要解释性建模的科研人员和决策者使用。
揭示复杂网络动态的支配方程是当代科学中的基础挑战，尤其在数据丰富的背景下，可揭示各类现象形成与演化的神秘模式与机制，并辅助决策。本文开发了一种通用计算工具，通过结合深度学习的强大拟合能力与预训练符号回归的方程推断能力，能够自动、高效、准确地学习复杂系统状态的符号变化规律。我们在物理、生物化学、生态学、流行病学等多个代表性场景中进行了大量实验验证。结果表明，该工具在性能和效率上显著优于当前最先进的符号回归技术。应用于全球疫情传播与行人移动等真实系统，进一步验证了其实际可用性。我们相信，该工具可作为通用解决方案，揭开复杂现象演变背后隐藏机制的迷雾，推动可解释性研究，并激发更多科学发现。
原文摘要 · Abstract (English)
Discovering governing equations of complex network dynamics is a fundamental challenge in contemporary science with rich data, which can uncover the mysterious 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 changing patterns of complex system states by combining the excellent fitting ability from deep learning and the equation inference ability from pre-trained symbolic regression. We conduct intensive experimental verifications on more than ten representative scenarios from physics, biochemistry, ecology, epidemiology, etc. Results demonstrate the outstanding effectiveness and efficiency of our tool by comparing with the 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 more scientific discoveries.
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