This project has a strong cognitive impact by advancing the theoretical and computational understanding of turbulent transport in magnetically confined plasmas, through the development of a large-scale numerical database and physics-informed machine learning surrogates.
It introduces efficient methodologies for exploring high-dimensional parameter spaces that are otherwise inaccessible to first-principles simulations, while training researchers in advanced plasma modeling, high-performance computing, and data-driven techniques.
The socio-economic impact is indirect and long-term, contributing to the international effort toward controlled nuclear fusion by reducing modeling uncertainty and computational cost, strengthening national research capacity, and supporting the development of sustainable energy technologies.