SciML: Automatic Discovery of droplet fragmentation Physics
We consider a classical droplet fragmentation problem in fluid mechanics, and augment the system modeling with neural architectures using DiffEqFlux.jl. This augmentation speeds up experimental inquiries by training physically-interpretable neural architectures to recover the physical equations for the spatial and temporal variation of dynamic quantities. Together we showcase how Julia's unique differentiable programming ecosystem can be the basis for next-generation physical science.
