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Learning Stochastic Closures Using Ensemble Kalman Inversion by Schneider, Tapio; Stuart, Andrew M.; Wu, Jin-Long is a scholarly article available to read on EtoBox.

What is Learning Stochastic Closures Using Ensemble Kalman Inversion about?

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. However, inconsistencies between SDEs and real data at short time scales often cause problems, when standard statistical methodology is applied to parameter estimation. The incompatibility between SDEs and real data can be addressed by deriving sufficient statistics from the time-series data and learning parameters of SDEs based on these. Following this approach, we formulate the fitting of SDEs to sufficient statistics from real data as an inverse problem and demonstrate that this inverse problem can be solved by using ensemble Kalman inversion (EKI). Furthermore, we create a framework for non-

Author

Schneider, Tapio; Stuart, Andrew M.; Wu, Jin-Long

Published

2020

Language

EN