In various sectors, such as manufacturing and engineering, data generation for data-driven models is expensive and time-consuming. For instance, material sciences often resort to destructive testing, while resource-intensive simulations, though an alternative to physical testing, pose computational demands. This project aims to address these challenges by developing machine learning methods that enhance accessibility and cost-effectiveness in data generation for resource-constrained settings.