融合物理模拟与深度集成，提升非线性系统异常预测精度
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles
- 用物理启发模拟器生成数据，结合CNN-LSTM提取时空特征
- 多模型集成输出，异常定位准确率显著优于单一模型
- 适合缺乏完整物理模型的工业与复杂系统早期故障监测
我们提出一种混合元学习框架，用于预测和检测具有非平稳与随机特性的非线性动力系统中的异常。该方法整合了模拟非线性增长-弛豫动态及随机扰动的物理启发模拟器，适用于众多复杂物理、工业与信息物理系统。采用CNN-LSTM进行时空特征提取，变分自编码器（VAE）实现无监督异常评分，孤立森林基于残差进行异常检测，并使用双阶段注意力循环神经网络（DA-RNN）在生成的模拟数据上进行一步预测。通过元学习器融合预测输出、重构误差与残差得分，形成综合异常预测。仿真实验表明，该混合集成模型在异常定位、泛化能力及对非线性偏离的鲁棒性方面均优于单个模型。该框架为非线性系统中缺陷早期识别与预测监控提供了一种广泛适用的数据驱动方法，尤其适用于难以获取完整物理模型的场景。
原文摘要 · Abstract (English)
We propose a hybrid meta-learning framework for forecasting and anomaly detection in nonlinear dynamical systems characterized by nonstationary and stochastic behavior. The approach integrates a physics-inspired simulator that captures nonlinear growth-relaxation dynamics with random perturbations, representative of many complex physical, industrial, and cyber-physical systems. We use CNN-LSTM architectures for spatio-temporal feature extraction, Variational Autoencoders (VAE) for unsupervised anomaly scoring, and Isolation Forests for residual-based outlier detection in addition to a Dual-Stage Attention Recurrent Neural Network (DA-RNN) for one-step forecasting on top of the generated simulation data. To create composite anomaly forecasts, these models are combined using a meta-learner that combines forecasting outputs, reconstruction errors, and residual scores. The hybrid ensemble performs better than standalone models in anomaly localization, generalization, and robustness to nonlinear deviations, according to simulation-based experiments. The framework provides a broad, data-driven approach to early defect identification and predictive monitoring in nonlinear systems, which may be applied to a variety of scenarios where complete physical models might not be accessible.
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