Learning Stochastic Closures Using Ensemble Kalman Inversion

Understand

Although the governing equations of many systems, when derived from first principles, may be viewed as known, it is often too expensive to numerically simulate all the interactions they describe.

• Therefore researchers often seek simpler descriptions that describe complex phenomena without numerically resolving all the interacting components.
• Stochastic differential equations (SDEs) arise naturally as models in this context.
• The growth in data acquisition, both through experiment and through simulations, provides an opportunity for the systematic derivation of SDE models in many disciplines.