Synthetic Model

Synthetic models are artificial datasets or environments used to train and evaluate machine learning models, particularly in situations where real-world data is scarce, expensive, or dangerous to collect. Current research focuses on improving the realism and utility of these synthetic datasets, employing techniques like generative adversarial networks (GANs) and autoregressive transformers to create diverse and representative data, and exploring methods to effectively transfer knowledge from synthetic to real-world domains. This work is significant because it enables the development and testing of AI systems in various fields, from autonomous navigation and medical image analysis to power grid stability and road safety, where real-world data acquisition is challenging. The resulting advancements in model training and evaluation are driving progress across numerous scientific and engineering disciplines.