Systematic physics constrained parameter estimation of stochastic differential 
equations

Peavoy, Daniel; Franzke, Christian; Roberts, Gareth O.

doi:10.1016/j.csda.2014.10.011

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Systematic physics constrained parameter estimation of stochastic differential equations

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

1312.1881.pdf
(Preprint), 2MB

Supplementary Material (public)

There is no public supplementary material available

Citation

Peavoy, D., Franzke, C., & Roberts, G. O. (2015). Systematic physics constrained parameter estimation of stochastic differential equations. Computational Statistics and Data Analysis, 83, 182-199. doi:10.1016/j.csda.2014.10.011.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0019-B7FB-E

Abstract

A systematic Bayesian framework is developed for physics constrained parameter inference ofstochastic differential equations (SDE) from partial observations. The physical constraints arederived for stochastic climate models but are applicable for many ?uid systems. A condition isderived for global stability of stochastic climate models based on energy conservation. Stochasticclimate models are globally stable when a quadratic form, which is related to the cubic nonlinearoperator, is negative de?nite. A new algorithm for the efficient sampling of such negative de?nitematrices is developed and also for imputing unobserved data which improve the accuracy of theparameter estimates. The performance of this framework is evaluated on two conceptual climatemodels.