用时空模型检测流体模拟中的异常，提升识别精度。
TRACE: Reconstruction-Based Anomaly Detection in Ensemble and Time-Dependent Simulations
- 对比2D与3D自编码器，利用时间上下文捕捉异常运动模式。
- 3D模型减少重复检测，对动态异常识别更鲁棒。
- 质量集中区域重建误差更大，影响异常判断。
高维、时变的模拟数据中异常检测面临复杂空间与时间动态的挑战。本文研究基于重构的异常检测方法，针对参数化卡门涡街模拟的集合数据，采用卷积自编码器进行分析。比较仅处理单帧的2D自编码器与处理短时序片段的3D自编码器。2D模型可识别单一时步的局部空间异常，而3D模型利用时空上下文，有效检测异常运动模式，并减少时间上的冗余报警。进一步评估体积化时变数据发现，重建误差受质量空间分布显著影响：集中区域误差明显大于分散配置。结果表明，时间上下文对动态模拟中稳健异常检测至关重要。
原文摘要 · Abstract (English)
Detecting anomalies in high-dimensional, time-dependent simulation data is challenging due to complex spatial and temporal dynamics. We study reconstruction-based anomaly detection for ensemble data from parameterized Kármán vortex street simulations using convolutional autoencoders. We compare a 2D autoencoder operating on individual frames with a 3D autoencoder that processes short temporal stacks. The 2D model identifies localized spatial irregularities in single time steps, while the 3D model exploits spatio-temporal context to detect anomalous motion patterns and reduces redundant detections across time. We further evaluate volumetric time-dependent data and find that reconstruction errors are strongly influenced by the spatial distribution of mass, with highly concentrated regions yielding larger errors than dispersed configurations. Our results highlight the importance of temporal context for robust anomaly detection in dynamic simulations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。