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Validation of state observers for high-rate structural health monitoring requires the testing of state observers on a large library of pre-recorded signals, both uni- and multi-variate. However, experimental testing of high-value structures can be cost and time prohibitive. While finite element modeling can generate additional datasets, it lacks the fidelity to reproduce the non-stationarities present in the signal, particularly at the higher end of the digitized signal's frequency band. In this preliminary work, generative adversarial networks are investigated for the synthesis of uni- and multi-variate acceleration signals for an electronics package under shock. Generative adversarial networks are a class of deep learning approach that learns to generate new data that is statistically similar to the original data but not identical and thus augmenting the data diversity and balance. This chapter presents a methodology for synthesizing statistically indistinguishable time-series data for a structure under shock. Results show that generative adversarial networks are capable of producing material reminiscent of that obtained through experimental testing. The generated data is compared statistically to experimental data, and the accuracy, diversity, and limitations of the method are discussed.