Machine Learning Stochastic Differential Equations: Applications in Reduced Order Models of Turbulence
The ubiquity of turbulence, as well as the importance and difficulty of its simulation are well-known. To combat the computational complexity, physically motivated "phenomenological" theories of reduced-order models have been hypothesized. While very interpretable, these theories struggle to match DNS results. We look to extend these phenomenological models, via neural networks. In this talk, I will focus mainly on our general training methodology to learn extensions to stochastic differential equations, and then touch on our applications to turbulence at the end.