Nonlinearly interacting system components often introduce instabilities that generate phenomena with new properties and at different space-time scales than the components. This is known as spontaneous self-organization and is ubiquitous in systems far from thermodynamic equilibrium. We introduce a theoretically-grounded framework for emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that capture how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones, local causal states, capture organized behaviors and coherent structures in complex spatiotemporal systems. Using our unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate the applicability of the local causal states for real-world domain science problems. We show that the local causal states capture vortices and their power-law decay behavior in two-dimensional turbulence. We then show that known (hurricanes and atmospheric rivers) and novel extreme weather events can be identified on a pixel-level basis and tracked through time in high-resolution climate data.

通过数据驱动算法和高性能计算，我们构建了一个基于时空光锥的框架，用于实现新现象的自组织，并且本文指出局部因果状态可以在复杂的时空系统中捕捉有序行为和一致结构。在二维湍流方面，我们证明了局部因果状态捕捉了涡旋及其幂律衰减行为，而在应用方面，我们演示了局部因果状态可用于识别已知（飓风和大气河流）和新的极端天气事件，并且可以通过高分辨率气候数据进行时间跟踪。

物理知识驱动表示学习：用于复杂动态系统中的新兴组织