To deal with the chaotic nature of the real world, researchers use data assimilation as part of any realistic simulation. Data assimilation allows us to modify the simulation results based on the real-world observations and deal with the exponentially growing error.
Understanding turbulent fluid flows lies at the heart of such versatile domains as climate, health, and energy. Modern methods used in flow simulations have yet to reach their efficiency limits in providing fast, high-resolution predictions. To answer outstanding questions about the nature of turbulence requires massive computing power and innovative methods designed with computational efficiency at their core.
The field of computational fluid dynamics (CFD) struggles with challenges to resolve computational complexity and cost of the simulations. Executing the operations over large fine meshes at small timesteps requires the algorithms to be designed with parallel efficiency at their core.