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

TL;DR

This paper introduces a deep learning approach using a conditional variational autoencoder to efficiently reconstruct acceleration signals from shock response spectrum curves, outperforming traditional methods in speed and accuracy.

Contribution

The paper presents the first data-driven inverse SRS reconstruction method using deep generative modeling, eliminating the need for iterative optimization.

Findings

Achieves higher spectral fidelity than classical techniques.

Demonstrates strong generalization to unseen spectra.

Inference is three to six orders of magnitude faster.

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.