Simulated Data

Simulated data is increasingly used to augment or replace real-world data in scientific research and machine learning applications, primarily to address data scarcity, high annotation costs, or safety concerns. Current research focuses on developing sophisticated simulation techniques, including generative adversarial networks (GANs), diffusion models, and various neural network architectures, to create realistic and diverse synthetic datasets that effectively bridge the domain gap between simulated and real data. This allows for more efficient model training, improved generalization, and the exploration of scenarios otherwise inaccessible or too expensive to obtain through real-world data collection, impacting fields ranging from autonomous driving to materials science and healthcare.