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This research applies conditional variational autoencoders (CVAE) to solve the ill-posed inverse problem of reconstructing shock time series from Shock Response Spectrum curves, offering a data-driven alternative to computationally expensive iterative optimization. By leveraging deep generative modeling, the approach achieves inference speeds three to six orders of magnitude faster than classical techniques while maintaining high spectral fidelity. This advancement demonstrates how machine learning can significantly accelerate signal processing tasks in dynamic system analysis.
Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning
arXiv:2603.03229v3 Announce Type: replace Abstract: 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.
Abstract: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.
Extended journal-style manuscript. 27 pages, 13 figures
Machine Learning (cs.LG); Signal Processing (eess.SP)
arXiv:2603.03229 [cs.LG]
(or arXiv:2603.03229v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.03229 Focus to learn more arXiv-issued DOI via DataCite
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