网络系统中基于扰动时间序列的物理信息结构推断的基本动力学单元

中文摘要 AI 辅助

在网络化动力系统中，首要的机制性兴趣参数是带符号的交互结构。从扰动时间序列数据中恢复该结构是一个基本的识别问题，且因三个相互耦合的障碍而变得复杂：交互架构的组合复杂性、有限干预下因果归因的模糊性，以及混淆结构推断的状态依赖动力学。每个障碍都源于结构，需要结构性的解决方案。我们通过采用还原论方法来解决这些挑战，引入基本动力学单元（FDUs）：带符号的三节点交互模式作为可组合的基元，将交互假设空间转换为有限、可构造且易处理的表现形式。我们表明，局部交互结构决定了将直接与中继影响区分开所需的扰动条件，使干预设计成为FDU表示的结构性结果。我们将FDU正则化的结构推断嵌入到一个物理信息神经常微分方程（ODE）中，其控制方程约束将结构假设转化为可验证的动力学预测，从而能够联合恢复交互结构和扰动解析轨迹。在具有已知真实值的合成基准上验证，该框架支持通过FDU基元表达的结构承诺、基序规定的干预设计以及物理信息学习，作为网络化动力系统中机制可解释推断的原则性基础。

英文摘要

In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure. Recovering this structure from perturbation time-series data is a fundamental identification problem, compounded by three coupled obstacles: the combinatorial complexity of interaction architectures, ambiguity of causal attribution under limited interventions, and state-dependent dynamics that confound structural inference. Each obstacle is structural in origin and calls for a structural solution. We address these challenges by adopting a reductionist approach, introducing Fundamental Dynamical Units (FDUs): signed three-node interaction patterns as composable primitives that convert the interaction hypothesis space into a finite, constructive, and tractable representation. We show that local interaction structure determines the perturbation conditions required to disentangle direct from relayed influence, making intervention design a structural consequence of the FDU representation. We embed FDU-regularized structural inference within a physics-informed neural ordinary differential equation (ODE) whose governing-equation constraint transforms structural hypotheses into verifiable dynamical predictions, enabling joint recovery of interaction structure and perturbation-resolved trajectories. Validated on synthetic benchmarks with known ground truth, the framework supports structural commitment, expressed through FDU primitives, motif-prescribed intervention design, and physics-informed learning, as a principled basis for mechanistically interpretable inference in networked dynamical systems.

发表机构

• Artificial Intelligence for Science Innovation, AstraZeneca（阿斯利康科学创新人工智能部门）

机构由 AI 辅助整理，请以论文原文为准。