Researchers are exploring the use of machine learning and generative modeling to better understand and predict earthquake ground motions. Various studies are utilizing different approaches, including deep learning, sequence to sequence learning, and generalized spectral inversion, to improve forecasting and modeling of seismic activity. The applications of these methods include real-time earthquake ground-shaking predictions, seismic data augmentation, and predicting aftershock ground motions.