1. 1) Amiri, G.G., Abdolahi Rad, A., Aghajari, S., and Khanmohamadi Hazaveh, N. (2012): Generation of near‐field artificial ground motions compatible with median‐predicted spectra using PSO‐based neural network and wavelet analysis, Computer‐Aided Civil and Infrastructure Engineering 27(9), 711-730. doi: https://doi.org/10.1111/j.1467-8667.2012.00783.x.
2. 2) Arjovsky, M., Chintala, S., and Bottou, L. (2017): Wasserstein Generative Adversarial Networks, in: Proceedings of the 34th International Conference on Machine Learning. (eds.) P. Doina & T. Yee Whye. (Proceedings of Machine Learning Research: PMLR).
3. 3) Florez, M.A., Caporale, M., Buabthong, P., Ross, Z.E., Asimaki, D., and Meier, M.A. (2022): Data‐Driven Synthesis of Broadband Earthquake Ground Motions Using Artificial Intelligence, Bulletin of the Seismological Society of America.
4. 4) Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014): Generative adversarial nets, Advances in neural information processing systems 27, 2672–2680.
5. 5) Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017): Improved training of wasserstein gans, arXiv preprint arXiv:.00028.