[2603.03229] Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning

Computer Science > Machine Learning arXiv:2603.03229 (cs) [Submitted on 3 Mar 2026] Title:Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning Authors:Adam Watts (1), Andrew Jeon (1), Destry Newton (1), Ryan Bowering (2) ((1) Los Alamos National Laboratory, (2) University of Rochester) View a PDF of the paper titled Inverse Reconstruction of Shock Time Series from Shock Response Spectrum Curves using Machine Learning, by Adam Watts (1) and 4 other authors View PDF HTML (experimental) 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 spe...