用流动守恒原理建模时空系统，提升动态耦合的物理可解释性。
FlowNet: Modeling Dynamic Spatio-Temporal Systems via Flow Propagation
- 基于流动守恒设计流传递机制，显式建模节点间动态耦合
- 在三个真实系统上七项指标均超越现有方法，性能显著提升
- 适合需要物理一致性与动态传播建模的研究场景
精确建模复杂的动态时空系统需捕捉流驱动的相互依赖关系和上下文敏感的交互动态。现有方法多基于图结构或注意力机制，依赖相似性驱动的连接假设，忽略了主导系统演化的非对称流交换。本文提出时空流（Spatio-Temporal Flow）这一受物理启发的范式，通过遵循守恒定律的可量化流转移，显式建模动态节点耦合。基于此，我们设计了FlowNet，利用流标记作为信息载体，通过流分配模块模拟源到目标的传递，确保状态重分布符合守恒律。自适应空间掩码模块动态调整交互半径，抑制无关噪声并实现上下文感知传播。级联架构提升了可扩展性与非线性表达能力。实验表明，FlowNet在三个真实系统上的七项指标中显著优于现有最先进方法，验证了其高效性与物理可解释性。本工作建立了一种基于时空流交互建模复杂系统的原理性方法。
原文摘要 · Abstract (English)
Accurately modeling complex dynamic spatio-temporal systems requires capturing flow-mediated interdependencies and context-sensitive interaction dynamics. Existing methods, predominantly graph-based or attention-driven, rely on similarity-driven connectivity assumptions, neglecting asymmetric flow exchanges that govern system evolution. We propose Spatio-Temporal Flow, a physics-inspired paradigm that explicitly models dynamic node couplings through quantifiable flow transfers governed by conservation principles. Building on this, we design FlowNet, a novel architecture leveraging flow tokens as information carriers to simulate source-to-destination transfers via Flow Allocation Modules, ensuring state redistribution aligns with conservation laws. FlowNet dynamically adjusts the interaction radius through an Adaptive Spatial Masking module, suppressing irrelevant noise while enabling context-aware propagation. A cascaded architecture enhances scalability and nonlinear representation capacity. Experiments demonstrate that FlowNet significantly outperforms existing state-of-the-art approaches on seven metrics in the modeling of three real-world systems, validating its efficiency and physical interpretability. We establish a principled methodology for modeling complex systems through spatio-temporal flow interactions.
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