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.

As the range of data platforms is rapidly evolving, the goal of this research project is to investigate and prototype upcoming architectures and technologies and to assess their applicability and potentials for industrial enterprises.