The ability to accurately predict multiscale fluid dynamics phenomena is essential for accurate weather forecasting, climate modeling, design of fusion energy devices and numerous other practical applications. However, the predictive modeling of multiscale phenomena is a challenging problem. Computer simulations solve the relevant equations at specific points that form a grid. Simulations on coarse grids can be made today, but they require accurate models of physics below the scale of the computational grid, i.e., between points on the grid. No general and systematic approach to construction of such sub-grid-scale models currently exists to capture all of the flow physics. The objective of this project is therefore to develop a systematic approach to modeling multiscale phenomena at a desired level of resolution. The fundamental advances resulting from the project will also impact a range of other applications in science (e.g., dynamics of accretion disks, forest/brush fires, pollution transport) and engineering (e.g., aircraft design, combustion and hypersonic vehicles). The project will also educate and train several graduate and undergraduate students in state-of-the-art numerical modeling techniques supplemented by novel machine-learning architectures, providing them with skills directly transferable to jobs in research and development, applied engineering applications, and national security applications. Traditional, analytic coarse-graining approaches for modeling