Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning
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 se...
| Authors | Adam Watts, Andrew Jeon, Destry Newton et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 31 August 2026 |
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