基于物理启发仿真与深度集成的非线性动力系统异常预测混合元学习框架
Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles
AI总结：
针对非线性动力系统的异常预测问题，提出融合物理启发仿真器与多种深度学习模型的混合元学习框架，经实验验证其在异常定位等方面性能优于单一模型，可用于无完整物理模型场景的早期缺陷识别。
摘要：
我们提出一种混合元学习框架，用于具有非平稳和随机特性的非线性动力系统的预测与异常检测。该方法整合了一个物理启发仿真器，可捕捉带随机扰动的非线性增长-弛豫动力学，这类动力学是许多复杂物理、工业和信息物理系统的典型特征。我们使用CNN-LSTM架构提取时空特征，用变分自编码器（Variational Autoencoders, VAE）进行无监督异常评分，用孤立森林（Isolation Forests）做基于残差的离群点检测，还在生成的仿真数据之上使用双阶段注意力循环神经网络（Dual-Stage Attention Recurrent Neural Network, DA-RNN）进行单步预测。为生成复合异常预测结果，这些模型通过一个元学习器整合，融合预测输出、重构误差和残差得分。基于仿真的实验表明，该混合集成模型在异常定位、泛化能力和对非线性偏差的鲁棒性上均优于独立模型。该框架为非线性系统中的早期缺陷识别和预测性监控提供了一种广泛适用的数据驱动方法，可应用于多种无法获取完整物理模型的场景。
英文摘要：
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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