Learning stochastic closures using ensemble Kalman inversion

DOI10.1093/imatrm/tnab003zbMath1478.60129arXiv2004.08376OpenAlexW3017093197MaRDI QIDQ5023844

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

Publication date: 25 January 2022

Published in: Transactions of Mathematics and Its Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.08376

zbMATH Keywords

stochastic differential equationinverse problemGaussian process regressionensemble Kalman inversionhierarchical parameterization

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Signal detection and filtering (aspects of stochastic processes) (60G35) Applications of stochastic analysis (to PDEs, etc.) (60H30) Geostatistics (86A32)

Related Items (7)

Mitigating Model Error via a Multimodel Method and Application to Tropical Intraseasonal Oscillations ⋮ A framework for machine learning of model error in dynamical systems ⋮ Reservoir computing with error correction: long-term behaviors of stochastic dynamical systems ⋮ A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parameterization ⋮ Recovering critical parameter for nonlinear Allen–Cahn equation by fully discrete continuous data assimilation algorithms ^* ⋮ Quantum Mechanics for Closure of Dynamical Systems ⋮ Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data

This page was built for publication: Learning stochastic closures using ensemble Kalman inversion