论文标题
深度学习揭示了复杂系统中的隐藏互动
Deep learning reveals hidden interactions in complex systems
论文作者
论文摘要
复杂系统的丰富现象长期以来吸引了研究人员，但是对系统微动力学进行建模并推断互动的形式对于传统数据驱动的方法来说仍然具有挑战性，这是由人类科学家建立的。在这项研究中，我们提出了一个由深度神经网络组成的无模型数据驱动的框架，该框架仅从观察到的数据中揭示和分析复杂系统中的隐藏相互作用。 AgentNet利用具有新颖可变注意力的图形注意力网络来模拟各个代理之间的相互作用，并采用了可以选择性地应用于任何所需系统的各种编码器和解码器。我们的模型成功捕获了各种模拟的复杂系统，即细胞自动机（离散），Vicsek模型（连续）和Active Ornstein-uhlenbeck颗粒（非毛基山脉），其中尤其是Agentnet可视化的注意值与真实的相互作用强度和表现出的集体行为相吻合，并且在培训中表现出了集体的培训数据，这是我们的互动强度和表现出的。来自一群鸟类的经验数据的演示表明，特工可以鉴定真实鸟类表现出的隐藏相互作用范围，而传统速度相关性分析无法检测到。我们希望我们的框架为研究复杂系统的研究开辟了一条新的途径，并提供对一般过程驱动建模的洞察力。
Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by human scientists. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system. Our model successfully captured a wide variety of simulated complex systems, namely cellular automata (discrete), the Vicsek model (continuous), and active Ornstein--Uhlenbeck particles (non-Markovian) in which, notably, AgentNet's visualized attention values coincided with the true interaction strength and exhibited collective behavior that was absent in the training data. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds, which cannot be detected by conventional velocity correlation analysis. We expect our framework to open a novel path to investigating complex systems and to provide insight into general process-driven modeling.
