用深度生成模型从冲击谱逆推加速度时序，速度快且精度高。
Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning
- 基于条件变分自编码器学习冲击谱到加速度信号的端到端映射
- 重建谱保真度优于传统方法，推理速度提升3~6个数量级
- 适合需要快速仿真冲击响应的工程场景
冲击响应谱（SRS）广泛用于表征单自由度（SDOF）系统对瞬态加速度的响应。由于从加速度时程到SRS的映射是非线性的且为多对一关系，从目标谱逆向重构时域信号本质上是病态问题。传统方法通过迭代优化求解，通常将信号表示为指数衰减正弦波之和，但这类方法计算成本高且受限于预定义基函数。本文提出一种条件变分自编码器（CVAE），学习从SRS到加速度时序的数据驱动逆映射。模型训练完成后，无需迭代优化即可生成符合指定目标谱的信号。实验表明，该方法在谱保真度上优于经典技术，对未见谱具有强泛化能力，推理速度提升三至六数量级。这些结果确立了深度生成建模在逆向SRS重构中的可扩展性与高效性。
原文摘要 · Abstract (English)
The shock response spectrum (SRS) is widely used to characterize the response of single-degree-of-freedom (SDOF) systems to transient accelerations. Because the mapping from acceleration time history to SRS is nonlinear and many-to-one, reconstructing time-domain signals from a target spectrum is inherently ill-posed. Conventional approaches address this problem through iterative optimization, typically representing signals as sums of exponentially decayed sinusoids, but these methods are computationally expensive and constrained by predefined basis functions. We propose a conditional variational autoencoder (CVAE) that learns a data-driven inverse mapping from SRS to acceleration time series. Once trained, the model generates signals consistent with prescribed target spectra without requiring iterative optimization. Experiments demonstrate improved spectral fidelity relative to classical techniques, strong generalization to unseen spectra, and inference speeds three to six orders of magnitude faster. These results establish deep generative modeling as a scalable and efficient approach for inverse SRS reconstruction.
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